Multifunction Radar Simulator (MFRSIM) - Defence …cradpdf.drdc-rddc.gc.ca/PDFS/unc03/p518679.pdfDRDC Ottawa TM 2002-165 iii Executive summary In 1993, Canada, Netherlands and Germany

Multifunction Radar Simulator (MFRSIM)

Sylvain Gauthier, Edwin Riseborough, Tim J. Nohara and Graeme Jones

Defence R&D Canada - Ottawa TECHNICAL MEMORANDUM

DRDC Ottawa TM 2002-165 December 2002

Multifunction Radar Simulator (MFRSIM)

Gauthier Sylvain Surface Radar Section

Riseborough, Edwin Surface Radar Section

Tim J. Nohara Sicom Systems Ltd

Graeme Jones Sicom Systems Ltd

Defence R&D Canada - Ottawa Technical Memorandum DRDC Ottawa TM 2002-165 December 2002

© Her Majesty the Queen as represented by the Minister of National Defence, 2002

© Sa majesté la reine, représentée par le ministre de la Défense nationale, 2002

DRDC Ottawa TM 2002-165 i

Abstract DRDC Ottawa has developed a multifunction radar (MFR) software simulator through several contracts with Atlantis Scientific Inc and Sicom System Ltd. The latest version, called MFRSIM, is coded in MATLAB version 6.1 (by MathWorks Inc), running on an IBM compatible PC. MFRSIM has been developed specifically to evaluate the detection capability of an MFR against anti-ship missiles (ASM) operating in a littoral environment. Both rotating and non-rotating phased array MFRs can be simulated, as well as conventional rotating antennas such as volume search radars. The simulation can include land clutter, sea clutter, chaff clutter, rain clutter and angel clutter with jamming. MFRSIM produces detection outputs in a causal manner one dwell at a time. The detector outputs are sent to the beam scheduler and the associated tracker. The tracker automatically initiates new tracks and interfaces with the beam scheduler to request tracking dwells. The beam scheduler controls the scheduling of surveillance; confirmation, cued search and tracking beams based on the tracker and detector outputs. The parameters of the transmitted dwell can be changed from dwell to dwell. A procedure has been developed to automatically select the waveform parameters for the scheduled dwell from a library of predefined waveforms. Although this capability is still rudimentary, the intention is eventually to select a given waveform as a function of the radar returns and environmental data. This report describes the major capabilities, features, models and applications of MFRSIM. The first section provides background information on the development of the DRDC Ottawa simulator over the years. The second section provides an overview of MFRSIM, including its modules and capabilities. The radar models of MFRSIM are examined in detail in the third section. This will provide a clear understanding of the capabilities and limitations of this simulator. The fourth section explains how to use MFRSIM and shows some examples using it. The last section discusses the future R&D plans for development of MFRSIM and its main applications.

Résumé RDDC Ottawa a développé un simulateur de radar multifonction a travers plusieurs contrats avec Atlantis Scientific Inc et Sicom System Ltd. La dernière version s’appelle MFRSIM et est codée en MATLAB version 6.1 (MathWorks Inc). MFRSIM fonctionne sur des ordinateurs personnels compatibles avec IBM. MFRSIM a été développé spécifiquement pour évaluer la capacité de détection des radars multifonctions contre les missiles anti-navires opérant en région côtière. Des radars multifonctions avec des antennes rotatives où fixes, à réseau de phase, peuvent être simuler par MFRSIM. Ce simulateur peut aussi simuler des antennes rotatives conventionnelles comme celle utiliser dans les radars de surveillance de volume. La simulation peut inclure du fouillis de sol, de précipitations, d’insectes, de plaquettes de brouillage, et de brouillage. MFRSIM produit des sorties de détection radar d’une manière causale de faisceau en faisceau Les sorties du détecteur sont envoyées au contrôleur de faisceau et au système de poursuite. L’algorithme de poursuite initie automatiquement les nouvelles traces de poursuite et communique avec le contrôleur de faisceaux. Les données produites par le contrôleur de faisceaux et du système de poursuite sont utilisées par le contrôleur de faisceaux pour planifiée la transmission des faisceaux de surveillance, de confirmation, et de poursuite. Les paramètres de la forme d’onde transmise peuvent être changer de faisceau en faisceau. Une procédure e été développé qui permet au contrôleur de faisceaux de choisir automatiquement les paramètres de la forme d’onde parmi un ensemble de forme d’onde prédéterminé. Cette capacité est encore rudimentaire. Cependant l’intention est éventuellement de choisir une forme d’onde en fonction de l’écho radar et des données d’environnement.

ii DRDC Ottawa TM 2002-165

Ce rapport décrit les capacités principales, les caractéristiques, les modèles et les applications de MFRSIM. La première section fournit des information sur le développement de MFRSIM au cours des années. La deuxième section fournit une vue d’ensemble sur MFRSIM incluant ses modules et capacités. Les modèles radars de MFRSIM sont examinés en détails dans la troisième section ce qui devrait donner une bonne compréhension sur les capacités et limitations de ce simulateur. La quatrième section explique comment utiliser MFRSIM avec quelques exemples. La dernière section décrit les futures directions de R & D pour MFRSIM ainsi que ses applications.

DRDC Ottawa TM 2002-165 iii

Executive summary In 1993, Canada, Netherlands and Germany signed a memorandum of understanding for the joint development of a ship-borne multifunction active phased array radar system (APAR). APAR is a wideband MFR operating in the I/J band and has four fixed antennas arrays with more than 3000 radar transmit-receive elements per face. In Canada, the Navy was considering installing APAR on the Halifax class frigates by 2005 as a mid-life upgrade. This radar, if acquired, would have been the most advanced radar ever deployed by the Canadian Armed Forces. DRDC Ottawa developed a simulator software to evaluate the detection capability of APAR-like radars against anti-ship missiles (ASM). Originally, the simulator could only simulate a fixed antenna face electronically scanning the horizon against one ASM flying straight-in at a constant speed. In the late 1990’s, it became apparent to DRDC Ottawa scientists that APAR couldn’t be fitted on the Halifax class frigates as originally intended. In fact, APAR is much too heavy to be installed on the Canadian patrol frigate (CPF) without major modifications to the ship’s structure. Hence, a new version of the MFR simulator was developed to investigate economic alternatives for the mid-life upgrade to the CPF radars. The simulation capabilities were significantly enhanced over the original version. For example, various configuration of MFR with multiple antenna faces, that can be rotating or fixed, can be simulated. Rotating MFRs such as ARABEL and MESAR with single or back-to-back faces, similar to APAR, have been studied in detail using this simulator. The simulator has also been used to evaluate the detection capability of the CPF radars against the most likely ASM missile to be encountered by the Canadian Navy. In 2000, MFR R&D requirements shifted toward developing a simulator that can be used to study adaptive techniques, including sensor integration, to optimize the MFR performance. MFR’s have a high level of agility in the transmitted waveform and antenna beam steering. This high beam flexibility makes it possible to change scan strategies or scheduling on the fly. Scheduling can be optimized as a function of the mission, detected threat and environment. MFRs can modify the transmitted waveform from pulse to pulse or burst to burst over a wide range of parameters. Depending on the environment and the threat, certain waveforms will provide a better performance than others. What is the best waveform or beam scheduling for a given scenario or environment? Most of the time, a radar operator would not know how to adequately exploit the versatility of the MFR. Current emphasis on studying littoral regions reduces the radar reaction time, making adaptive techniques more important. Sensor integration as suggested by the NAAWS study is also becoming much more important to win back reaction time [1]. The newest version of the MFR simulator, called MFRSIM, has been developed to study adaptive control of MFR. The simulated MFR can change the parameters of the transmitted waveforms from dwell to dwell, conduct random beam scan, and receive cues from separate radars. It is planned that other sensors will be added to cue the MFR. The present simulator, MFRSIM is a very capable MFR software simulator. The major capabilities, features, models and applications of MFRSIM are described in this report. The first section provides background information on the development of the DRDC Ottawa simulator over the years. The second section provides an overview of MFRSIM, including its modules and capabilities. The radar models of MFRSIM are examined in detail in the third section. This will provide a clear understanding of the capabilities and limitations of this simulator. The fourth section explains how to use MFRSIM and shows some examples using it. The last section discusses the future R&D plans for development of MFRSIM and its main applications.

Gauthier, S., Riseborough, E., Nohara T.J., Jones. G. 2002, Multifunction Radar Simulator (MFRSIM). DRDC Ottawa TM 2002-165. Defence R&D Canada - Ottawa

iv DRDC Ottawa TM 2002-165

Sommaire En 1993, le Canada, les Pays-Bas et l’Allemagne ont signé un protocole d’entente pour le développement conjoint d’un radar navale à réseau de phase actif (APAR). APAR est un radar multifonction (RMF) à large bande opérant dans la bande I/J et a quatre antennes fixe à réseaux avec plus de trois milles éléments de transmission et réception pour chaque face. Au Canada, la marine canadienne considérée l’installation de APAR sur les frégates de classe Halifax vers 2005 pour une mise a jour. Ce radar aurait été le radar le plus sophistiqué présentement déployée par les forces Armées Canadienne. RDDC Ottawa a développé un logiciel de simulation pour évaluer les capacités de détection de radar similaire à APAR contre des missiles anti-navires. Originalement, le simulateur pouvait simuler seulement une antenne fixe à réseau balayant électroniquement l’horizon contre des missiles anti-navires volant en ligne droite et vitesse constante. A la fin des années 1990, il est devenu évident aux scientifiques du RDDC Ottawa que APAR ne pourrait pas être installée sur les frégates canadiennes comme originalement voulu. En faits APAR est beaucoup trop lourd pour être installée sur les frégates canadiennes. Cela demanderait des modifications considérables à la structure du navire. On a donc développé une nouvelle version de simulateur de RMF pour étudier des alternatives économiques au sujet de la mise à jour des composants radars des frégates. Les capacités de simulation ont été améliorées considérablement comparée à la version précédente. Par exemple, différentes configurations de RMF ayant plusieurs antennes, rotatives ou fixes, peuvent être simulées. Des RMFs avec une où deux antennes rotatives ont été étudiées en détails à l’aide de ce simulateur. ARABEL et MESAR sont des exemples de RMFs utilisant des antennes rotatives. Ces RMFs, cependant, requièrent l’utilisation de missiles actifs plutôt que semi-actifs. Le simulateur a aussi été utilisé pour évaluer les capacités de détection des radars installée sur les frégates canadiennes contre des missiles anti-navires que pourrait affronter la marine canadienne. En 2000, les besoins de R & D sur les RMFs ont nécessité un simulateur qui permettrait d’étudier des techniques de contrôle adaptatives incluant l’intégration de récepteurs pour optimiser les performances radars. Les RMFs ont un grand degré de flexibilité dans la forme d’onde qui est transmise et le balayage du faisceau électronique. La grande flexibilité du faisceau rend possible un changement de stratégie de balayage électronique en temps réel. Le contrôle des faisceaux peut être optimiser en fonction de la mission, de la menace qui est détectée et de l’environnement. Les RMFs peuvent changer la forme d’onde transmise d’impulsion en impulsion. Dépendant de l’environnement et de la menace, certaines formes d’ondes fourniront une meilleure performance que d’autres. La présente emphase sur les régions côtières réduit considérablement le temps de réaction rendant les techniques adaptatives plus importantes. L’intégration de récepteurs comme suggérer par une étude de l’OTAN (NAAWS study) est aussi en train de devenir plus important pour augmenter le temps de réaction. La dernière version du simulateur s’appelle MFRSIM et a été développée pour étudier des techniques de contrôle adaptatif de RMFs. Aujourd’hui, MFRSIM est un logiciel de simulation qui est très capable. Ce rapport décrit les capacités principales, les caractéristiques, les modèles et les applications de MFRSIM. La première section fournit des informations sur le développement de MFRSIM au cours des années. La deuxième section fournit une vue d’ensemble sur MFRSIM incluant ses modules et capacités. Les modèles radars de MFRSIM sont examinés en détails dans la troisième section ce qui devrait donner une bonne compréhension des capacités et limitations de ce simulateur. La quatrième section explique comment utiliser MFRSIM avec quelques exemples. La dernière section décrit les futures directions de R&D pour MFRSIM ainsi que ses applications.

Gauthier, S., Riseborough, E., Nohara T.J., Jones, G. 2002. Multifunction Radar Simulator (MFRSIM). DRDC Ottawa TM 2002-165. R & D pour la défense Canada - Ottawa

DRDC Ottawa TM 2002-165 v

Table of contents

Abstract........................................................................................................................................ i

Executive summary ................................................................................................................... iii

Sommaire................................................................................................................................... iv

Table of contents ........................................................................................................................ v

List of figures ........................................................................................................................... vii

1. INTRODUCTION......................................................................................................... 1

2. BACKGROUND........................................................................................................... 2 2.1 Multifunction Array Radar Simulator .............................................................. 2

2.1.1 APAR a Naval MFR............................................................................ 2 2.1.2 MFARSIM........................................................................................... 3

2.2 Rotating Multifunction Array Radar Simulator................................................ 4 2.2.1 Economical alternatives to APAR....................................................... 4 2.2.2 RMFARSIM........................................................................................ 5

3. MFRSIM OVERVIEW ................................................................................................. 9 3.1 Adaptive Multifunction radar ........................................................................... 9 3.2 Simulation capabilities ..................................................................................... 9 3.3 Modules overview .......................................................................................... 10

4. RADAR MODELS...................................................................................................... 14 4.1 Radar Range Equation.................................................................................... 14

4.1.1 Missile Models .................................................................................. 14 4.1.2 Antenna Models ................................................................................ 14 4.1.3 Propagation Models........................................................................... 15 4.1.4 Clutter Models. .................................................................................. 16

4.1.4.1 Sea Clutter.......................................................................... 16 4.1.4.2 Land Clutter ....................................................................... 18 4.1.4.3 Rain and Chaff Clutter ....................................................... 18

vi DRDC Ottawa TM 2002-165

4.1.4.4 Angel Clutter...................................................................... 18 4.1.4.5 Jammer Models .................................................................. 19

4.1.5 Receiver Models................................................................................ 19 4.2 Post Processing Models.................................................................................. 20

4.2.1 Doppler Filtering ............................................................................... 20 4.2.2 Probability of Detection .................................................................... 20

4.2.2.1 Square-Law Detector ......................................................... 21 4.2.2.2 M-of-N Detector ................................................................ 21

4.2.3 Radar Measurements Models ............................................................ 22

5. USING THE SIMULATOR........................................................................................ 24 5.1 Introduction .................................................................................................... 24 5.2 MFRSIM Architecture.................................................................................... 24

5.2.1 Simulator Modules ............................................................................ 24 5.3 Running MFRSIM.......................................................................................... 25

5.3.1 Modes of Operation........................................................................... 26 5.3.2 Starting MFRSIM.............................................................................. 27 5.3.3 Running a Basic Simulation (Calculator Mode)................................ 28 5.3.4 Running A Basic Simulation (Full Mode)......................................... 29

6. FUTURE R&D DIRECTIONS ................................................................................... 36

7. References ................................................................................................................... 37

List of symbols/abbreviations/acronyms/initialisms ................................................................ 39

DRDC Ottawa TM 2002-165 vii

List of figures

Figure 1. Artist’s impression of APAR fit on a frigate............................................................... 2

Figure 2. MFR tasks ................................................................................................................... 3

Figure 3. MFARSIM scenarios .................................................................................................. 4

Figure 4. RMFARSIM basic scenario ........................................................................................ 6

Figure 5. Firm track range histograms........................................................................................ 6

Figure 6. PPI displays................................................................................................................. 7

Figure 7. MFR Optimization study............................................................................................. 8

Figure 8. Cueing Capability.......................................................................................................... 10

Figure 9. MFRSIM Main Modules........................................................................................... 11

Figure 10. MHMT tracker from Sicom Systems Inc ................................................................ 13

Figure 11. Terpem Output ........................................................................................................ 16

Figure 12. Surface Clutter ........................................................................................................ 17

Figure 13. High-level representation of MFRSIM architecture ......................................................... 25

Figure 14. Flow diagram of a typical simulation ru ......................................................................... 27

Figure 15. The starting window with the File menu highlighted........................................................ 28

Figure 16. SIR plot from calculator mode (default parameters + Doppler processing)........................ 28

Figure 17. The plot menu ............................................................................................................ 29

Figure 18. Beam patterns for full simulation run ............................................................................ 30

Figure 19. Missile parameter window ........................................................................................... 31

Figure 20. PPI views of missile, Sea Giraffe, APAR and SPS-49 detections (clockwise from top left) . 33

Figure 21. Adjusting the Sea Giraffe's search parameters for tracker interfacing............................... 34

Figure 22. Segment of tracker display window after simulation run.................................................. 34

Figure 23. PPI display of the APAR face's tracking beams ............................................................. 35

viii DRDC Ottawa TM 2002-165

List of tables

Table 1. List of α and β Values ................................................................................................ 21

DRDC Ottawa TM 2002-165 1

1. INTRODUCTION DRDC Ottawa has developed a MFR software simulator through several contracts with Atlantis Scientific Inc and Sicom System Ltd [2to 9]. The source code is the property of the Crown. The MFR simulator has had different names over the years such as MFARSIM, RMFARSIM, and ADAPT_MFR. The latest version ADAPT_MFR shall be referenced hereafter as MFRSIM and stand for MFR simulator. MFRSIM is coded in MATLAB version 6.1 (by MathWorks Inc), and run on an IBM compatible PC. DRDC Ottawa has developed a MFR software simulator through several contracts with Atlantis Scientific Inc and Sicom System Ltd. The latest version, called MFRSIM, is coded in MATLAB version 6.1 (by MathWorks Inc), running on an IBM compatible PC. MFRSIM has been developed specifically to evaluate the detection capability of an MFR against anti-ship missiles (ASM) operating in littoral environment. Both rotating and non-rotating phased array MFR can be simulated, as well as, conventional rotating antennas such as volume search radars. The simulation can include land clutter, sea clutter, chaff clutter, rain clutter and angel clutter with jamming. MFRSIM produces detection outputs in a causal manner for one dwell at a time. The detector outputs are sent to the beam scheduler and the associated tracker. The tracker automatically initiates new tracks and interfaces with the beam scheduler to request tracking dwells. The beam scheduler controls the scheduling of surveillance; confirmation, cued search and tracking beams based on the tracker and detector outputs. The parameters of the transmitted dwell can be changed from dwell to dwell. A procedure has been developed to automatically select the waveform parameters for the scheduled dwell from a library of predefined waveforms. Although this capability is still rudimentary, the intention is eventually to select a given waveform as a function of the radar returns and environmental data. This report describes the major capabilities, features, models and applications of MFRSIM. The first section provides background information on the development of the DRDC Ottawa simulator over the years. The second section provides an overview of MFRSIM, including its modules and capabilities. The radar models of MFRSIM are examined in detail in the third section. This will provide a clear understanding of the capabilities and limitations of this simulator. The fourth section explains how to use MFRSIM and shows some examples using it. The last section discusses the future R&D plans for development of MFRSIM and its main applications.

2 DRDC Ottawa TM 2002-165

2. BACKGROUND

2.1 Multifunction Array Radar Simulator

2.1.1 APAR a Naval MFR In 1993, Canada, Netherlands and Germany signed a memorandum of understanding for the joint development of a ship-borne multifunction (APAR [10 to 14]. APAR is a wideband MFR operating in the I/J band and has four fixed antenna arrays with more than 3000 radar transmit-receive elements per face. Figure 1 shows an artist’s representation of APAR on a frigate [10]. In Canada, the Navy was considering installing APAR radar on the Halifax class frigates by 2005 as a mid-life upgrade [12, 13]. This radar, if acquired, would have been the most advanced radar ever deployed by the Canadian Armed Forces.

Figure 1. Artist’s impression of APAR fit on a frigate.

MFRs have a high level of agility in the transmitted waveform and antenna beam steering. MFRs are usually phased array radars that can be electronically scanned in both azimuth and elevation. Hence, they can look in a specific direction almost instantaneously. There is no need to wait for the next revisit, as in conventional rotating radar. The high beam flexibility makes it possible to perform several functions in a single system. Typically, the duties of a naval MFR include, horizon search and track, surface search and track, own missile tracking with uplink data transmission and target illumination, high diver tracking, cued acquisition as from adjunct sensors, burnthrough in self-screening jamming, track-on-jammer, non-cooperative target recognition, and possibly kill assessments [1]. The various duties of MFR are shown diagrammatically in Figure 2. MFR perform all these functions sequentially. Hence, time to be spent on each function has to be optimized. APAR is not intended to be the only radar onboard, but would be part of a suite of sensors as recommended by a NATO Anti-Air Warfare System (NAAWS) study conducted in early 1990’s. The NAAWS study recommended suite of sensors that would be able to defeat the emerging threats of new ASMs [1]. The preferred configuration consisted of a suite of four sensors including a precision electronic support measures sensor, an infrared search and track sensor dedicated to horizon surveillance, a volume surveillance radar, and a MFR [1]. The keys functions of the MFR under the NAAWS configuration were horizon search and support to target engagement. Horizon search is


aimed at detecting and tracking sea-skimming missiles, which are the greatest threat to the navy. These missiles have small radar cross-section and fly at low-altitude which make early detection extremely difficult. They also fly at very high speed, which means that they have to be detected as soon as possible, otherwise, it might not be possible to successfully engage them.

Figure 2. MFR tasks

2.1.2 MFARSIM In early 1990’s, DRDC Ottawa developed a software program called MFARSIM, which stands for multifunction array radar simulator [1]. MFARSIM has been developed specifically to evaluate the detection capability of APAR-like radars against ASMs. This simulator can only simulate a fixed antenna array face scanning electronically the horizon against one ASM that flies in a straight line and at constant velocity (Figure 3). MFARSIM has three major modules, which are the scenario generation, the radar range equation and the post processing modules. The scenario generation module generates the missile trajectories and all antenna beam positions in one pass and determines when they intersect. Only these specific dwells are passed to the next module of MFARSIM, the radar range equation module. This approach considerably reduces the computer processing requirements, since the full range equation is run for much fewer dwells positions. The radar range equation module calculates the signal-to-noise ratio (SNR) that will be produced at the output of the radar receiver. This module uses the usual form of the radar range equation for this calculation. The effects of anomalous propagation are included in MFARSIM. Data files of propagation loss factors are calculated using the commercial software called PCPEM [15] and read into MFARSIM. The post-processing module calculates the probability of detecting the missile using the resulting SNR. The first version of MFARSIM had no models of clutter. It assumed that clutter has been suppressed successfully. Curves of signal-to-noise ratio and the probability of detection are the main outputs of this simulator. MFARSIM was used to investigate the impact of beam spacing and anomalous propagation on the performance of MFR. Beam spacing has a significant impact on the time spent on horizon search and


needs to be optimised. In 1993, MFARSIM was upgraded to include multiple PRFs per dwell, interlaced beams, verification dwells and false alarms. The upgraded version was used to study the impact of frequency burst assignments in the search dwells on the performance of MFR [16]. In March 1995, the capabilities of MFARSIM were significantly extended to include the effect of clutter phenomena and the resulting signal to interference ratio [4]. Models of sea, rain and chaff clutter were included into MFARSIM. Radar modifications designed to improve performance such as Doppler processing, pulse canceller, sensitivity time control, and M-of-N detector were also modeled. Several horizon search scenarios involving varying clutter conditions were investigated using this new version of MFARSIM.

dx

M F R rad a r

T arg e ted sh ip

S E A S K IM M E R

dy

Figure 3. MFARSIM scenarios

2.2 Rotating Multifunction Array Radar Simulator

2.2.1 Economical alternatives to APAR In late 1990’s, the Surface Radar Section started studying economic MFR alternatives to APAR for the mid life CPF upgrade. At that time, it became apparent to DRDC Ottawa scientists that APAR couldn’t be fitted on the Halifax class frigates as originally intended. APAR is much too heavy to be installed on the CPF without major modifications of the ship’s structure [16]. Extensive studies have investigated potential solutions to fitting APAR on the to Halifax class frigate, but none were approved. One of the options considered, cutting the ship in two sections and adding a ten-meter plug to accommodate the new systems. The idea was abandoned due to its prohibitive cost. All other options have also been discarded. Alternative MFRs of interest included rotating MFR with one face or two back-to-back faces. ARABEL and SAMPSON are good examples of naval rotating MFRs. ARABEL is a passive phased array rotating MFR operating at I/J band. SAMPSON is a dual rotating active phased array MFR operating at E/F band. These MFRs, however, requires the use of active missiles, rather than semi-active, or separate illuminators. Semi-active missiles, such as the Sea Sparrow and Evolved Sea Sparrow will be the missiles used in the CPF. The plan would be to keep the STIRs already on the CPF to perform the target illumination for the semi-active missiles.


2.2.2 RMFARSIM In 1999, a new version of the MFR simulator was developed to study economic alternatives to APAR, such as rotating MFRs, for the CPF mid life upgrade. This version was called "RMFARSIM" which stands for "Rotating Multifunction Array Radar Simulator” [5]. The main modules of RMRARSIM are the scenario generation, the radar range equation and the post analysis modules. The simulation capabilities have been significantly enhanced over the older version. RMFARSIM can simulate scenarios with up to five ASM (sea skimmers or high divers) operating in littoral regions (Figure 4). Each ASM can manoeuvre in both azimuth and elevation. Littoral land clutter models and jamming were added. The simulated MFRs can have multiple antenna faces that can be rotating or fixed. RMFARSIM can also simulate conventional rotating search radars. Modeled MFR functions include horizon search (or volume search), tracking, cueing, and confirmation. For the rotating MFR, the missiles are only visible for a fraction of the time when the antenna faces are oriented in their direction. Therefore, confirmation, tracking, and cueing must be scheduled when the missiles are visible. This is one of the main differences between fixed and rotating antenna faces. For the non-rotating case, after MFR functions, the antenna beam returns to its prior azimuth position relative to the boresight. The propagation-modeling software PCPEM used to calculate the propagation loss path was replaced by TERPEM and integrated directly into RMFARSIM [17]. Monte Carlo simulation capabilities have also been added to RMFARSIM. In fluctuating systems, a Monte Carlo algorithm generates fluctuating quantities based on their statistics. In RMFARSIM, Monte Carlo simulation is used to produce radar-like detection events. Full Monte Carlo simulation runs, to ascertain system performance, would be extremely time consuming, as the complete simulator would have to be run many times. RMFARSIM circumvents this to generate feasible results by taking a post-detection Bayesian approach. We generate a uniform random number between 0 and 1 for each range bin where the Pd has been calculated. This is then repeated for 1000 equivalent Monte Carlo runs. Based on the random number generated for each range bin, detection is declared if this number is lower than the probability of detection for that range. Otherwise there is no detection. We define the earliest detection range (or tracking range) as the first range bin (in descending order) that has a successful detection. This procedure is repeated for all 1000 Monte Carlo events. These results are then “histogrammed” to show the detection or tracking range distribution. An example of a Monte-Carlo histogram generated by RMFARSIM is shown in Figure 5. There are only a few detections that occur around 15 km where the sea skimmers crossed the horizon. Later, hundreds of detections occur around 13.7 km, and so on. The low spot around 13.5 km is due to blind zones caused by eclipsing loss. The Monte Carlo histograms may prove very useful to show the best case and worst-case scenarios. For example, for the worst case around 12.4 km, there might be not enough time to intercept the missile. As a result, the user would experiment with the radar sensitivity to move the worst-case result to a desired range. PPI displays showing where detections occur have also been added to RMFARSIM. Figure 6 shows example of RMFARSIM displays for different scenarios.


TARGETED SHIP

JAMMER

- MULTIFUNCTION RADAR- VOLUME SEARCH RADAR

Figure 4. RMFARSIM basic scenario

Figure 5. Firm track range histograms


SINGLE ROTATING FACEBACK TO BACK

ROTATING FACES

FOUR FIXED FACES

Figure 6. PPI displays

RMFRSIM has been used to study the optimization of various configurations of an MFR against the most likely ASM scenarios to be encountered by the Canadian Navy [6, 7]. During this study, the detection capabilities of existing radar components installed on the CPF were also evaluated against these ASM scenarios. The study includes the Sea Giraffe and the SPS-49 as well as one and two rotating MFR faces each similar to an APAR face. A special emphasis of the study was put on detection of sea skimmers in littoral waters. Rotating MFRs with single or back-to-back faces, similar to those of APAR, have been studied in detail using the simulator. The simulator has also been used to evaluate the detection capability of the CPF radars against the most likely ASM missile threat to be encountered by the Canadian Navy. As expected, the Canadian Navy is now looking to alternative radar solutions for the CPF mid-life upgrade (FELEX program) and our DRDC Simulator is being used to aid in this assessment.


SPS-49 RADAR SEA-GIRAFFE

Figure 7. MFR Optimization study


3. MFRSIM OVERVIEW This section provides an overview of the latest version of the DRDC Ottawa simulator, which is referred to here as MFRSIM and referred to as Adapt_MFR by Sicom System ltd in the reference document. MFRSIM has been developed specifically to study adaptive techniques for the optimization of MFR performance against ASMs.

3.1 Adaptive Multifunction radar In 2000, MFR R&D requirements shifted toward developing a simulator that can be used to study adaptive techniques, including sensor integration and optimization of the performance of MFR. MFRs such as APAR have a high level of agility in the transmitted waveform and antenna beam steering. This flexibility makes it possible to change scan strategies or scheduling on the fly. Scheduling can be optimized as a function of the mission, the detected threat and the environment. MFRs can modify their transmitted waveform from pulse to pulse or burst to burst over a wide range of parameters (frequency, pulse repetition interval and pulsewidth). Depending on the environment and the threat, certain waveforms should provide a better performance than others. What is the best waveform or beam scheduling for a given scenario or environment? Most of the time, a radar operator would not know how to adequately exploit the versatility of the MFR. The naval AN/SPY-1 radar is a good example of an advanced MFR that can modify its waveform and beam scheduling (as well as signal processing) to adapt to different missions, environments, and threats. AN/SPY-1 radar is a passive phased array MFR operating at E/F band using four fixed planar antennas. Current emphasis on studying littoral regions reduces the radar reaction time requirements, making adaptive techniques more vital. Sensor integration increases, as suggested by the NAAWS study, is being investigated to win back reaction time. In order to study the adaptive control of an MFR, the Surface Radar Section has developed a new version of MFR simulator called MFRSIM [8, 9]. MFRSIM has first been made completely causal or event driven, as required for the studying. In contrast with RMFARSIM, which generates a pre-defined matrix in batch, Monte Carlo capabilities have not be implemented with MFRSIM. MFRSIM was further modified to enable the transmission of dynamic waveforms, random beam scans, and cueing from separate radars.

3.2 Simulation capabilities MFRSIM can simulate various configurations of MFRs operating against ASMs in littoral regions. The modeled radars can have multiple antenna faces that can electronically scan in both elevation and azimuth. Each antenna face can be set as an independent radar that can be either rotating or not rotating. The electronic beam position is defined relative to the antenna boresight, and is independent of the mechanical rotation. The simulated MFRs can perform the following functions: horizon or volume search, detection, confirmation, tracking, and cued volume search. Horizon search (or volume search) is the primary function being interrupted to perform confirmation, tracking, or cued volume search. Detection is followed by a confirmation dwell after a given latency time. Tracking is initiated after confirmation has been successful. An adjunct radar such as volume search radar (VSR) can cue the MFR. Different cuing strategies can be selected by the user such as “cued before confirmation”. Once a target is detected by the VSR, the location is”cued” to the MFR, which then initiates a small volume search around the cued location. Conventional volume search radars take a few rotations to confirm detection. In order to gain time and take advantage of the MFR beam flexibility, the MFR can be cued after the initial detection and the MFR will perform the confirmation, thus saving a few rotations. The MFR will then initiate tracking and be ready to support target engagement.


The basic scenario that can be simulated by MFRSIM is shown in Figure 4 and has not changed much since the development of RMFARSIM. Up to five ASMs heading directly towards a targeted ship can be simulated. Radars installed on another ship, offset by a specified distance (dx, and dy), perform the detection of the ASM. This approach makes it possible to simulate either area or point air defence systems by choosing the appropriate values of dx, and dy. Each missile can be defined by up to four flight legs. The user specifies the values of the ASM parameters at the start and end of each flight leg. MFRSIM will then compute the trajectory of the ASM using a smoothing process. For the scenarios that are defined in littoral region, MFRSIM will automatically extract the digital terrain elevation data (DTED) when available. The range profile of the land in direction of the illuminated missile is extracted and used in the calculation of land clutter and propagation effects. The simulation can include land clutter, sea clutter, chaff clutter, rain clutter and angel clutter with jamming. The user has control over both the scenario environment, and the radar systems parameters. MFRSIM produces detection outputs in a causal manner for one beam (position) at a time. The detector outputs are sent to the beam scheduler function and the associated tracker. The tracker automatically initiates new tracks and interfaces with the beam scheduler to request tracking dwells. The beam scheduler controls the scheduling of surveillance, confirmation, cued search and tracking beams based on the tracker and detector outputs. The parameters of the transmitted dwell can be changed from dwell to dwell. A procedure has been developed to automatically select the waveform parameters for the scheduled dwell from a library of predefined waveforms. Although this capability is still rudimentary, the intention is to eventually select a given waveform as a function of the radar returns and environmental data. The associated tracker and PPI display can receive and display detection outputs time-sequentially (Figure 9).

Figure 8. Cueing Capability

3.3 Modules overview The main modules of MFRSIM are the beam generator, the detection module, and the beam scheduler.


MHMTTRACKER

MFRSIM

BEAM GENERATION

DETECTION MODULE

BEAM SCHEDULER

- DETECTION EVENT?- RANGE, ANGLE, VELOCITY

R, AZ, EL, V

PREDICTION

- WHAT, WHERE, WHEN

Figure 9. MFRSIM Main Modules

The beam scheduler generates the position of each antenna beam in horizon search mode until a detection occurs. The beam position for the horizon search can be generated sequentially, i.e., from a minimum to a maximum value. The search scan pattern can also be generated randomly as is done by the MESAR and SPY-1 radars. MFRSIM can be set to conduct volume search rather than horizon search. The volume search can also be done sequentially according to a given pattern (row by row) or randomly. MESAR not only conducts random volume scan but also changes the parameters of the waveforms as a function of desired range. MFRSIM generates the scenario one-time increment at a time. A pre-detection check is done for each antenna position to determine if there is a possibility of a detection and if the full radar range equation calculation has to be performed. For this purpose, two thresholds have been set; the antenna gain in the target direction and a simple form of SNR. If the antenna gain in the direction of the illuminated missile is superior to an input threshold, then there is a possibility of having detection. This triggers looking at the next threshold. The second threshold is a simple function of signal to noise ratio, which does not include the propagation factor and clutter level. If this SNR is above a given threshold detection is possible and then the whole detection algorithm is run. The detection process includes the calculation of the mean power using the standard form of the radar range equation and the associated spectrum that will be produced at the receiver output. In effect, MFRSIM does not generate a signal that propagates to the target and returns. The propagation factor includes the propagation effects from multipath and atmospheric conditions. The propagation factor F is calculated by the commercial program TERPEM developed by Signal Sciences Limited in the United Kingdom [17]. For all scenarios, including littoral regions, MFRSIM can read DTED files to automatically extract the land profile to be used in TERPEM to determine the propagation factor.


TERPEM is directly interfaced with MFRSIM and is completely transparent to the user. The types of clutter that are modeled in MFRSIM are sea, land, rain, chaff, and angel. The models used are given in many radar books and open literature on radars. Receiver models include eclipsing losses, sensitivity time control, thermal noise and so on. From these models, MFRSIM calculates the spectrum of the mean power at the output of the receiver, which is a combination of the missile, clutter, jammer, and noise power. The clutter contribution to the output of the receiver is first reduced through Doppler filtering (moving target indicator (MTI) , Doppler filter bank (DFB), or both in series). The resulting signal to residual clutter ratio is then used to calculate the probability of detection Pd . These probabilities of detections are then used to produce radar-like returns to determine where detection occurs. A uniform random number between 0 and 1 is generated, and detection is declared if this number is lower than the probability of detection for that range. Otherwise there is no detection. The detection process includes the calculation of the mean power using the usual form of radar range equation and associated spectrum that will be produced at the receiver output. In effect, MFRSIM does not generate a signal that propagates to the targets and comes back. It simply calculates a mean power using the usual form of the radar range equation. The propagation factor has to be calculated to include the propagation effects from multipath and atmospheric conditions, which are very important for low-level propagation. The commercial program TERPEM calculates the propagation factor F. For scenarios, including littoral regions, MFARSIM can read DTED files and automatically extract the land profile to be used in TERPEM for the propagation factor calculation. TERPEM is directly integrated into MFRSIM and is completely transparent to the user. The simulation program considers four types of processing; no processing, n-pulse canceller, DFB, and an n-pulse canceller with a DFB. In order to determine the output of the filter, one needs to know their power spectral responses, which are given in several radar books [4]. The resulting signal to residual clutter ratio is then used to calculate the probability of detection Pd . The mathematical engine used to calculate the probability of detection is based on the Neuvy equation [1, 23]. These probabilities of detections are then used to produce radar-like detection statistics, i.e., to show where detection occurs. A uniform random number between 0 and 1 is generated, and a detection is declared if this number is inferior to the probability of detection. Otherwise there is no detection. When detection is declared, radar measurements similar to those produce by real radar will be generated and passed to the beam scheduler. Standard accuracy formulas given by reference [8] are used to generate the radar measurements. The beam scheduler will update its detection list and communicate the radar measurements to an external tracker, proprietary to Sicom System ltd [8]. The tracker will initiate tracking and return the predicted location of the targets. The associated tracker and PPI display can time-sequentially receive and display detection outputs (Figure 10). The beam scheduler controls the scheduling of surveillance, confirmation, cued search and tracking beams based on the tracker and detector outputs. The scheduler includes advanced logic for rotating MFRs to ensure that the target can be illuminated at the desired time. The parameters of the transmitted signal can be changed from dwell to dwell.


Figure 10. MHMT tracker from Sicom Systems Inc

In the search mode, the waveform can be changed sequentially or randomly from dwell to dwell or scan to scan. One hopes that by changing the waveform from dwell to dwell, one would be able to detect the target faster. For the confirmation dwell, the user has the option of using the same waveform as in the detection process. For the tracking dwell, the waveform is adjusted automatically as a function of the target velocity and range. The pulse repetition frequency (PRF) is first adjusted to minimize eclipsed ranges. Then if necessary the target is moved out of the blind zone by moving the radar frequency upward by 500MHz. MFRSIM takes into account false alarms and generates false radar measurements when they occur. Using the probability of false alarms per burst input by the user, the probability of false alarm per dwell can be calculated. Using a process similar to the one used in generating the detection event, MFRSIM generates a random number and if this number is inferior to the probability of false alarm then a false alarm is declared. Once false alarms are declared, MFRSIM will generate random radar measurements over the whole range of possible parameters. For example, the value of false measured range is selected randomly between the minimum and maximum possible range. A similar process is done for the generation of speed and direction measurements. The false measurements are sent to the scheduler, which acts upon them as real detections, i.e., they are send to the tracker for processing.


4. RADAR MODELS This section describes the models and assumptions used to represent the physical processes to be simulated by MFRSIM.

4.1 Radar Range Equation The radar range equation function calculates the mean power and associated spectrum produced at the receiver. In effect, MFRSIM does not generate a signal that propagates to the target and back. It simply calculates mean power using the standard form of the radar range equation. Mathematical models are needed to include the effects of the propagation conditions, clutter, jammers, and receiver. Once these models are defined the calculation is straightforward. MFR can transmit several bursts per dwell, and can change frequency and PRF from burst to burst. The mean power at the output of the receiver is then calculated for every burst.

4.1.1 Missile Models The first step is the calculation of the mean power that will be reflected back from the target to the receiver input. The mean power reflected by the missile Pm is calculated using Equation (1).

=

rs

mpeakm L

FLR

GPP

4

43

22

)4( π

σλ (1)

where λ = wavelength (meter), Ppeak = Transmitted peak power (watts), G = Antenna Gain in the target direction, σm = Radar cross section of the missile (meters square), R = Missile range (meter), Ls = System loss, Lr = Atmospheric loss, F = Propagation factor.

The Doppler effect shifts the power reflected by the target in frequency. This frequency shift, fd, is proportional to the radial motion of the target, Vr, and is calculated using Equation (2).

λ

rd

Vf

2= (2)

4.1.2 Antenna Models A characteristic of an MFR is that the antenna beam can be electronically scanned off the antenna boresight. Scanning the antenna beam off-boresight broadens the beam and reduces its overall beam gain. This effect is included in the antenna model by incorporating the scan angle in the gain and beamwidth functions.

23

00 )cos(;

)cos( sss

s GGBW

BW θθ

==

(3)

where BW0 and G0 are the antenna beamwidth and gain on boresight, and BWs, Gs are the antenna beamwidth and gain off-boresight [1]. The off-boresight scanning angle is given by θs. The cosine θs compensates for the loss in antenna aperture. The asymmetric antenna illumination is defined by the 3/2 power in the gain expression. MFRSIM gives the option of using non-adaptive or adaptive scans. In non-adaptive search both dwell time and azimuth are kept constant over the whole scan. In an adaptive scan, both the dwell and


azimuth step can be increased to compensate for a reduction in gain and broadening of the beam. In our model, the reduction in gain is compensated by transmitting more coherent pulses per burst nc according to Equation (4).

)0()(

)0()(

2

=

== φ

φφ

φ cs

sc n

GG

n

(4)

where φ is the azimuth angle [1]. Similarly, reducing the spacing between adjacent beams can also compensate for the reduction in gain. Simulations have shown that adaptive scanning can significantly increase the detection range. Adaptive scanning, however, increases the percentage of the time that the horizon search takes up.

4.1.3 Propagation Models The propagation models determine the propagation factor F, along with the rain attenuation. The backscattering of rain is discussed in Section 4.1.3.4. The rainfall loss γ in dB/km is given by

brra=γ (5)

where rr is the rainfall rate in millimeters per hour [4], a and b are coefficients described in [18], and [19]. The atmospheric loss Lr is calculated by multiplying the value of γ by the rain cell size along the propagation path. The propagation factor F is defined as the ratio of the electric field that exists in the presence of the earth's atmosphere and surface divided by the electric field that would exist in free space. The propagation factor includes the effects of reflection, refraction, and diffraction. The propagation factor F is calculated using the commercial program TERPEM developed by Signal Sciences Limited, U.K. [17]. TERPEM is based on the parabolic equation,

0)1),((2 222

2

2

2

=−+∂∂

+∂∂ uyxmk

xuik

zu

(6)

where u is the electric or magnetic field component, k is the wave number, and m is the modified refractive index. The parabolic equation is a very popular and powerful tool for solving radiowave propagation problems. It outputs a very good approximation of the full electromagnetic wave equation [20, 21]. This equation is solved numerically through marching techniques. The user specifies the initial values of the electric field at or close to the antenna. A solution is found for the first iterative cell by satisfying the boundary conditions at the bottom and the top of the cell. This solution is then passed to the next cell and so on. An advantage of the marching technique is that it can handle changing conditions along the range coordinates. The solution u(x,z) represents the electric field that would be measured. The electric field that would exist in free space can be calculated using the radar range equation. The propagation factor F can then be calculated from these two values of the electric fields [17]. Figure 11 shows an example of the output produced by TERPEM. The usual lobed structure caused by multipath interference is easily recognizable. We can also see that electromagnetic energy propagates beyond the horizon due to ducting effects and to a lesser extent diffraction. For our simulator, TERPEM is directly integrated into MFRSIM and is completely transparent to the user.


10 GHz, A ntenna polarization: Vertical, Duct 10 m eter

Figure 11. Terpem Output

4.1.4 Clutter Models. The types of clutter that are modeled in MFRSIM are sea, land, rain, chaff, and angel. The models are given in [4].

4.1.4.1 Sea Clutter The power reflected by the sea clutter Psea for each burst is calculated by integrating the power reflected by the illuminated sea surface patches.

ϕξϕξϕ

π

λddRR

RRxG

LLP

P sea

R

Rsr

peaksea

BWnc

c

= ∫∫ 4

2

3

2 ),,(),()4(

2)(

0

2

1

(7)

where Ppeak = Transmitted peak power (watts), λ, and BW = Wavelength (meter) and beamwidth (radian as for ϕ, and ξ), G(ϕ, ξ) = Antenna gain in the direction of the sea surface patches,

seax = Clutter patch mean reflectivity (dBm2/m2), and assumed to constant over the illuminated cell Lr and Ls = Rain attenuation and system loss, Rc1 and Rc2 are the limits of the range resolution cell (meter).

The angular limits of integration extend several beamwidths (n) past the maximum beam centerline to include the contribution from the whole antenna pattern [4]. This is equivalent to integrating from zero to n(BW) and then multiplying the result by 2 as is done in Equation (7).


Figure 12. Surface Clutter

The model used to calculate the mean reflectivity of the sea is taken from the NATO Above-Air Warfare System (NAAWS) study initiated in late 1980's [18]. This study was driven by the growing threat of sea-skimming and high diving missiles [1]. The NAAWS sea clutter model is an empirical model in which the mean reflectivity (in dBm2/m2) is calculated as

dpsgrefsea KKKKxx ++++= (8)

where refx is the mean reference reflectivity and the K terms are correction factors.

The reference value refx is the mean reflectivity measured at a given frequency for sea state 5, 0.1 degree grazing angle, vertical polarization, and zero degree wind direction. The K correction factors calculate the mean reflectivity correction for different conditions of grazing angle, sea state, polarization, and wind direction [18]. The frequency spectrum of the sea clutter Ssea(f) is assumed to be Gaussian [4] as in Equation (9).

2

2

2)(

)2()( sea

seaff

sea

seasea e

PfS σ

σπ

−−

=

(9)

The mean Doppler frequency of the sea, fsea, and the standard deviation αsea, are calculated as

λφφν )cos(2.0 wawind

seaf−

=

(10)

λφφν

α)cos()125.0(2.0 wawind

sea−

=

(11)

where windν is the mean wind speed, wφ is the wind direction and aφ is the beam azimuth angle. These equations define the sea clutter spectrum.


4.1.4.2 Land Clutter

The power reflected by the land clutter, Pland, is calculated by integrating over the illuminated area as in

Equation (7) except that seax is replaced with landx . The mean reflectivity of the land surface is given

by Equation (12). 4

0 Fσxland = (12) ψσ sin0 Γ= (13)

In this model, the mean reflectivity is a function of the grazing angle ψ, the propagation factor F, and the normalized reflectivity Γ function of the land type and transmitter frequency [5]. The propagation factor is calculated using TERPEM. Once again, this model is based on empirical data.

4.1.4.3 Rain and Chaff Clutter The power reflected by rain or chaff is calculated by integrating over the illuminated volume [4] as shown in Equation (14).

dVLLR

xGPP

sr

tchafforrain 43

22

)4( πλ∫= (14)

The value of mean reflectivity x for rain and chaff is calculated by

cchaffrrain

rain WxrK

x λλ

94

6.1

1022; −×== (15)

where Krain is a constant, and Wc is the chaff density in g/km3 [4]. The frequency spectrum of the rain and chaff clutter is assumed to be Gaussian as in Equation (9). The mean frequency "f" and standard deviation "α" are calculated as

λσσ

αλ

φφν 222.0;

)cos(2.0 turbshearvolume

wawindvolumef

+=

−= (16)

where σshear and σturb describe the effects of wind shear and turbulence. Their values are given in reference [0]. These models are all describe in radar books and based on experimental data [4].

4.1.4.4 Angel Clutter Angel clutter is produced by birds or insects. For insects the mean reflectivity is modeled as follows [5],

Hzfiff

xx cc

ri9

4

9 103.3;103.3

×<

×= (17)

Hzfiff

xx cc

ri9

3

9 103.3;103.3

×>

×= (18)

where rx is the pre-defined reference reflectivity at 3.3 GHz as shown below. Insect Clutter Type Elevation Limits

rx (m2/m3) Surface Layer 700-1000m 10-11 Elevated Layer 700-3000m 10-9

For birds, a model based on density of the flock (ρr) and relative RCS gain variations (β1, β2) in decibels is used to determine the reflectivity [5]:


=

−

×

291102.1

log

10ββ

ρcf

rbx (19)

The Doppler spectrum of the clutter is modeled as Gaussian with a center frequency based on linear velocity and with a spread dependent on the wing-beat rate [5].

4.1.4.5 Jammer Models MFRSIM can simulate either spot or barrage jammers. A spot jammer transmits all of its energy at the radar transmission frequency, whereas a barrage jammer transmits on a bandwidth wider than the radar bandwidth. The jamming power that will reach the radar receiver depends on the distance and angular position of the antenna beams. Both of them are accounted for by MFRSIM, so the jamming power that reaches the radar can be determined. MFRSIM gives the user the option of employing beam nulling. The user specifies the value of the nulling improvement factor, and then the jamming power is attenuated accordingly. If the target is close in azimuth to the jammer direction, then its received mean power is also attenuated, modulated by the beam shape and offset from the center of the null.

4.1.5 Receiver Models In above sub-sections, we were able to calculate the mean power due to the missile, clutter, and jamming that arrives at the input of the receiver. A portion of this power can arrive at the receiver while it is blanked off. The receiver is blanked off when the radar is transmitting a pulse. The power arriving at that time is simply lost. An MFR usually operates with a high duty cycle that can produce significant eclipsing losses, especially if high PRFs are used. The reflected signal is completely lost when the target range is a multiple of the range ambiguity Runamb(m). In MFRSIM, the eclipsing loss Le is calculated from Equation (20).

RmRRRmRwhereR

mRRmR

RmRRmRwhereR

mRR

L

unambunambunamb

unambunambunamb

e

∆+≤≤∆

+

∆

−∆+

∆+≤≤

∆

−

=

)(2

)(;)()(2

2)()(;

))((2

α

α

(20)

∆R is the size of the uncompressed range resolution cell [3]. Sensitivity time control can be used to reduce reflected power clutter at short-ranges. In MFRSIM, the receiver gain is modified according to Equation (21).

00mod)( g

RRRggg

n

unamb

unambrx +

−= (21)

where g and g0 are the maximum gain and the gain offset at zero range respectively [4]. The user chooses the value of the range exponent n. Usually, n=3 for land and sea clutter reduction and n=2 for rain clutter rejection. MFRSIM also includes simple models for receiver non-linearity and power due to thermal, tropospheric, and phase noises [5]. From these models, we can calculate the spectrum of the mean power at the output of the receiver, which is a combination of the missile, clutter, jammer, and noise power.


4.2 Post Processing Models The role of the post-processing function is to calculate the probability that a target will be detected. The clutter contribution to the output of the receiver is first reduced through Doppler filtering. This produces a resulting signal to residual clutter ratio that is then used to calculate Pd. MFRSIM can also generate a Monte-Carlo-like simulation by transforming the calculated Pd into a detection histogram.

4.2.1 Doppler Filtering The simulation program considers four types of processing: no processing, n-pulse canceller, Doppler Filter Bank (DFB), and an n-pulse canceller with a DFB. In order to determine the output of the filter, we need to know their power spectral responses. The power spectral response Hnc of an n-pulse canceller is given by

20 )](sin2[)( TffH nn

nc π= (22) where T0 is the pulse repetition interval, and n is the number of time delays [4, 22]. The power spectrum response of a DFB [4] is given by Equation (23).

|2|

1)()2( oc Tfj

N

nDFB enWfH ππ −

=∑=

(23)

where N is the number of Doppler filters, fc is the center frequency of the filter bank, and W(n) is the filter coefficient weights [19] which are given by Equation (24).

02)( Tfnj cenW = (24)

Using these equations, the resulting signal to noise ratio at the output of the corresponding filter can be easily calculated [4]. From this ratio, we can calculate the probability of detection for a given probability of false alarm.

4.2.2 Probability of Detection The mathematical engine used to calculate the probability of detection is based on the equation of Neuvy [1, 23] which is given by Equation (25).

β

α

1

32

)2(lnlog

;10

== −

SNRn

PuwhereP

nc

faud

(25)

The value of the parameters α and β are given in Table 1. The equations of Neuvy are based on the theory of Swerling and Marcum [24, 25] and give Pd when nnc pulses are incoherently integrated.


Table 1. List of α and β Values

SWERLING MODEL α VALUE β VALUE

0 1 + 2 exp(-nnc/3) 0.167

1 0.67(1 + 0.67 exp(-nnc/3) 1

2 1 0.167 + exp(-nnc/3)

3 0.75(1 + 0.67 exp(-nnc/3) 0.67

4 1 0.67 exp(-nnc/3)

4.2.2.1 Square-Law Detector MFRSIM models both the square law detector and the M-of-N detector [4]. The square law detector applies a threshold on the resulting SNR after the pulses are incoherently integrated. For the MFR, bursts of each dwell are integrated together. The probability of detection can then be calculated directly from Equation (25) using nnc equal to the number of bursts. Although we use the equation of Neuvy to model the square law detector, this is not strictly correct. The equations of Neuvy have been developed for a SNR that has a constant mean and standard deviation from pulse-to-pulse. In our case, the frequency can change from burst-to-burst and consequently the mean and standard deviation will change with each burst. For example, the impact of multipath and ducting are very dependent on frequency. In order to use the equation of Neuvy, we calculate a mean SNR as follows.

∑=

=bursts

nnSNR

burstsRNS

#

1#1

(26)

This is the value of SNR that is used in Equation (25). The equations of Neuvy were developed for sinusoidal signals competing against white Gaussian noise. In our model, the values used for the SNR are the outputs from the Doppler filters, which may contain residual clutter or jamming power and hence provide different statistics to white gaussian noise. In any case, MFRSIM currently uses the equations of Neuvy to calculate the probability of detection. In the future, however, more accurate expressions for these calculations may be available.

4.2.2.2 M-of-N Detector An M-of-N detector uses two thresholds to decide if a target is present or not. A first threshold is applied to each of the N video pulses to be integrated. A counter that counts the number of times the first threshold is exceeded follows this. Detection is reported when the second counter reaches a certain value M. The M-of-N detectors are easy to implement and are more resistant to noise and sea spikes. Usually, the probability to exceed the first threshold is the same for all events. In this case, the probability that the first threshold will be exceeded at least M times in N events is calculated from Equation (27).

∑=

−

−=−=

N

MiiN

iNd

idiNd iNi

NcppCP)!(!

!;)1()(

(27)

Pd is the probability of detection of the dwell and pd the probability of detection of the bursts [26]. However, in our simulation, the SNR changes from burst-to-bust and so the probability of detection per


burst will also change. In this case, the probability that the first threshold will be exceeded at least M times in N events is given by

∑ ∑ ∏= − =

=

N

Mi

nsCombinatioAll

missesMNhitsM

N

jd jburstofyprobabilitP

))"(,( 1 (28)

where the probability of the burst j is its probability of detection or non-detection depending on whether it is a hit or a miss. The j burst is considered a hit if the threshold is exceeded, otherwise it is a miss. The summations within the bracket represent the sums of all combinations having M hits, and (N-M) misses. Equation (28) might seem difficult to implement on a computer since we need to generate all combinations of M-of-N events. Speed is an important consideration, due to the large number of combinations that must be calculated for each missile intercept. There is a simple solution to implement Equation (28). One simply counts in base 2 from zero to the largest number with N digits. For example, let us generate all of the combinations of three binary events. Each event may either have a value of 0 or 1. The maximum value of a three-digit number in base two is 111, which is 7 in base 10. Counting from zero to 7 in base then generates all the combinations. In our model, the zeros represent the misses while the ones are the hits. A matrix N

MIc ≥ of all combinations having at least M hits on N events can be built by removing all other combinations. The zeros and ones have to be

replaced by the appropriate probability of detection. Two vectors dPr

and ndPr

are built using the probability of detection and non-detection of each burst within the dwell.

−

−−

=

=

Nburstofd

twoburstofd

oneburstofd

nd

Nburstofd

twoburstofd

oneburstofd

d

P

PP

P

P

PP

P

1""""

11

;""""

rr

(29)

The matrix of appropriate probability for all combinations of at least M hits is then obtained from

ndN

MidN

MiN

Mi PCPCMrr

)( ≥≥> += δ (30)

where δ represents the Kronecker delta function and operates on each element of the matrix.

==

=1;00;1

)(xifxif

xδ

(31)

The Pd for the M-out-of-N detector can now be calculated from Equation (28) by multiplying the element of each row of the matrix probability, N

MiM >, and then summing all the rows together.

4.2.3 Radar Measurements Models The above probabilities of detections are then used to produce radar-like detection events to show where detection occurs. A uniform random number between 0 and 1 is generated, and detection is declared if this number is inferior to the probability of detection. Every time a detection event is declared, MFRSIM will generate radar measurements similar to what a real radar would produce. This is done using a standard accuracy formula described in reference [8]. This formula states that the measured data is a function of the exact coordinate plus a standard deviation function of SNR. This formula is used to generate the target range, speed, and bearing as seen by realistic radar. For each coordinate, a standard deviation is also calculated to give the measurement accuracy. Once the radar measurements have been generated they are sent to the scheduler that will then update all detection outputs. The radar measurements are then send to the external tracker through a software communication interface. The tracker initiates tracking and returns the next predicted position to the


scheduler. Using this information, the scheduler will choose what will be the next dwells and system parameters.


5. USING THE SIMULATOR

5.1 Introduction This section provides a brief overview on the use of MFRSIM. Additional details can be found in the online help files, which are part of the software distribution, as well as the Final Report (Volume 1 and Volume 2) of Contract W7714-020624 which provide a software reference manual and a high-level user document, respectively.

5.2 MFRSIM Architecture An illustration of the high-level simulation architecture is presented in Figure 5-1. The framework consists of a series of modules (left hand side) that are used to prepare, process or interpret any significant system element or generated simulation data. In practice, they are defined through the user interface, and stored in associated data structures. The simulation flow located in the centre section of the figure represents the running code, which makes use of the data and associated functionality (algorithms, models etc.). The right side represents any external elements that may be interfaced or “plugged in” to the simulator code; for example, Sicom’s MT-Tracker.

5.2.1 Simulator Modules The simulation modules provide all relevant parametric information needed by the simulator, and are obtained through a graphical user interface (GUI). Modules are provided for the specific models (e.g. radar, environment) which the simulator will use during execution. Tools are also provided to assist the user in understanding the effects of entered parameters for particular models. For example, a display tool showing the missile trajectory that results from the output of the missile dynamics model aids in designing useful scenarios.


Figure 13. High-level representation of MFRSIM architecture There are many plotting options available within MFRSIM that serve as analytical, diagnostic and output display tools. Diagnostic information can be gleaned from many plots, which can display a separation of the power levels of targets, clutter and noise, as well as the individual performance of the various processing options (Doppler, MTI filtering, non-coherent integration). MFRSIM also provides pre-run analytical plotting tools, such as blind zone and (DTED) terrain displays, which allow the user to determine appropriate radar parameter settings. A post-simulation output display is also provided, which displays the actual recorded and processed plots on a simulated PPI screen.

5.3 Running MFRSIM The diagram in Figure 5-2 represents the flow of a typical simulation run. The large rectangular boxes in the centre of the diagram detail the actual sequence of events and computations that are generally performed by the simulator. As may be seen, the simulation execution is composed of several branches. These branches seek to maximize the throughput by doing no more processing than is necessary (e.g.


by not processing a detection that has no chance of being “seen”). In order to ensure that no detection opportunities are missed the values of these settings is determined by the user.

5.3.1 Modes of Operation As a result of the large parameter sets, and general versatility of the tool, there are many and varied modes in which the simulator may be operated. There are, however, three basic modes of operation, which are:

- calculator mode - simulation mode without tracker - simulation mode with (MT) tracker

To aid in obtaining quick results and for preliminary radar system evaluation, a radar calculator mode has been included in the package. In this mode, a stripped, non-causal version of the simulator is run, with a constant velocity missile approaching the radar’s (locked) azimuth boresight, either at a constant height or along the antenna’s elevation boresight. In the presence of only system noise and surface back-scatter interference, results illustrating properties such as propagation factors, SNRs, SIRs and detection probabilities are produced. TERPEM may be used in the calculator mode. The other two modes (simulation mode with and without tracker) are causal in nature and provide a complete simulation run, making available to the user all the functionality of MFRSIM.


Figure 14. Flow diagram of a typical simulation ru

5.3.2 Starting MFRSIM To start MFRSIM, start MATLAB and set the path to include all directories containing the MFRSIM files. Next, in the MATLAB command-window type adapt_mfr and then <enter>. This brings up the main MFRSIM window, which is shown below in Figure 5-3.


Figure 15. The starting window with the File menu highlighted

5.3.3 Running a Basic Simulation (Calculator Mode) In order to run any simulator, the user must first either load a pre-generated signal file (using the File >> Load Parameters from File... selection, seen in the figure above), or use the default parameters (with File >> Set Default Parameters). For this initial example, we can set the default parameters, which automatically load in a straightforward simulation scenario. The user is now in a position to run a simulation. By selecting File >> Run the Calculator Mode, a stripped-down simulation is initiated. After a calculator mode has executed, a large number of diagnostic display windows appear that show various properties and performance indicators. These plots detail propagation factor and path loss, clutter, missile and noise powers, and detection probabilities. A sample signal to interference ratio (SIR) plot that produced with the default parameters and Doppler processing selected appears below.

Figure 16. SIR plot from calculator mode (default parameters + Doppler processing)


5.3.4 Running A Basic Simulation (Full Mode) A complete simulation run can also be performed using the same default parameters from the initial example. To start the run, press the File>> Run Simulation without Tracker selection (shown in Figure 5-3). During the run, timing information appears in the main MATLAB command window, and a “Simulation Run Finished” message is posted there following the run’s conclusion. After the simulation run has been completed, the user may examine the results, together with some diagnostic information, by accessing the Plot pull-down menu, which is shown below. Most plotting options are self-explanatory and can be further referenced through the help files. If the user selects the Plot >> Plot beam patterns/missile intersections… option, and displays the results from 7.5 to 9 seconds, three plots will appear, one of which is shown in Figure 5-6.

Figure 17. The plot menu


Figure 18. Beam patterns for full simulation run

The beam pattern plots of the type shown in Figure 5-6 are a unique feature of MFRSIM. The entire history of the radar beam’s scan pattern and the missile trajectory, or a portion thereof, can be displayed, in both azimuth and elevation, for three different antenna coordinate systems (electrical centre, mechanical centre and absolute). These plots provide a rich source of information, and it is recommended that users become familiar with their form and interpretation. The two angles of interest are of course the elevation angle and the azimuth angle of the radar beam. Beams (surveillance, confirmation, tracking and cueing) are represented by different coloured circles in Figure 5-6 and the missile trajectory by coloured lines. For the current example, both surveillance (blue) and confirmation (green) beams are displayed. When intersections occur at a particular time, for example around 7.8 seconds and 8.7 seconds, one or more beams (indicated by circles) will of course appear on or near the corresponding missile trajectory. These intersections are easily seen in Figure 5-6. Whenever we have intersections, the actual antenna gains at the intersection points are calculated; these are displayed in the bottom graph of Figure 5-6. Before detailed radar calculations (such as SIR and detection) are performed, the antenna gains must be large enough to make detection possible, The causal nature of a basic simulation (Full Mode) arises because beams are directed and radar processing (detection and estimation) is performed from one dwell to the next, dictated by a resource manager that decides when and where to paint a particular beam, as would be the case for an operational radar. Much more complex radar systems can be modelled than the default radar example just considered. The following example presents a much more complex situation. A simulation is created that uses multiple radars in a single simulation. In particular, three radars are modelled: the Sea Giraffe (a rotating C-


band radar), the SPS-49 (a rotating UHF-band radar) and a single APAR-face radar (a fixed, electronic scanning multifunction radar). In each case, radar specifications are obtained, and then translated/mapped onto parameters available through the numerous windows available in MFRSIM. The radar and processing parameters are organized into seven windows: search region, waveform, antenna, receiver, processing, cued search, and waveform adaptivity. Each of these windows needs to be updated appropriately for each radar that is to be modelled for simulation. Defining the environmental settings is a straight forward operation. By selecting the environment option from the parameter menu, five parameter windows are accessible, organized as follows: general, volume clutter, surface clutter, angel clutter, anomalous propagation. Again, the user walks through each of these screens and makes the appropriate settings. In the example that follows, only surface clutter is modelled; all other sources of clutter are turned off. Lastly, the trajectory and dynamics of the incoming missile(s) must be specified. The window, selected with Parameters >> Missile, is shown below.

Figure 19. Missile parameter window As seen in Figure 5-7, a very simple missile trajectory is chosen. It travels in a straight line, starting 30 km out from the target towards the target, from due North at a speed of about Mach 1.5. The missile travels 10 m above the sea surface, and has an RCS of 0.75 m2. Figure 5-7 reveals that much more complicated flight paths are possible. The parameters are divided into two sections; the top three rows establsih initial conditions and the bottom three allow specific characteristics to be specified for any number of trajectory “legs”. Within this framework, the user is able to define complex flight paths by


altering dynamical and cross section behaviour in a piece-wise sense. A simple flight will, however, suffice for this example. The missile’s trajectory may be viewed using the Plane View and Trajectory Profile options from the Parameter pull-down menu. Having defined our three radars, environment and missile trajectory, the user is now in a position to run the simulation. The simulation runs in a similar manner as during the default run performed earlier. Timing information (with the occasional diagnostic) appears in the MATLAB window, and, depending on the speed of the user’s computer, a progress bar may be spotted when beam intersections are being processed. After the simulation has been run, the user would like to know if it has been successful, or, more particularly, whether the target missile was detected. Probably the best way to check this is to open a PPI display, available from the pull-down plot menu (Plot>> Open PPI Display…). The following four plots show different views of the PPI display for this example.


Figure 20. PPI views of missile, Sea Giraffe, APAR and SPS-49 detections (clockwise from top left) Figure 5-8 clearly shows that detections were received and processed by all radars. With knowledge of radars involved, the results seem plausible. The APAR face has clearly performed the best, with “rock solid” detections, which are hardly surprising since it possesses the highest resolution and power-aperture product and fastest revisit time (there is no rotation). The Sea Giraffe has detected the target most of the time, with the dropouts probably due to the rotation. The SPS-49 made some detection at the further ranges but seems to have had dropouts at nearer ranges. This may be explained by the change in waveform the SPS-49 applies for ranges under 20 km, which is for chaff suppression. It is a simple matter to use the tracker; the File >> Run Simulation option (Figure 5-3) does it by default. It is, however, important to ensure that the radar parameters are appropriate for tracker operation and interaction. They can be checked and adjusted through the SEARCH RADAR REGION PARAMETER GROUP window. For this example, we shall only interface the Sea Giraffe and APAR systems to the tracker. The portion relevant to the new Sea Giraffe settings is reproduced below.


Figure 21. Adjusting the Sea Giraffe's search parameters for tracker interfacing Figure 5-9 shows that we are now interfacing with the tracker. This produces a two-fold effect – it enables the radar to send confirmed detections to the tracker for track association and update, as well as letting the radar request predictions for utilisation of tracking. As shown above, we have chosen not to utilise any tracking beams for the Sea Giraffe, since it is a fixed antenna radar without monopulse; the track request interface retrieves 3-D information which is more suited to beam agile systems such as APAR. We have already set up APAR’s tracking parameters and track time loading, thus it is only necessary to ensure that it is selected (“yes”) to interface with the radar. The tracker appears once the run has been started, and remains after the conclusion of the simulation until the user presses a key (while in MATLAB’s main window). A snapshot of part of the tracker’s display is shown below, where, for illustrative purposes, we have turned the persistence up so the complete track history is available. The result has produced a solid track at the expected location. Figure 5-11 below also shows the PPI display of the tracking beams from APAR, which consistently has painted the missile throughout its trajectory.

Figure 22. Segment of tracker display window after simulation run


Figure 23. PPI display of the APAR face's tracking beams


6. FUTURE R&D DIRECTIONS MFRSIM is a very capable radar simulator that can be used in a wide range of applications. It has been used to evaluate the performance of the radar components installed on the CPF against ASM scenarios [7]. The most likely ASM scenarios to be encountered by the Canadian Navy in open sea and littoral region were included in this study. MFRSIM has also been used to optimize the radar parameters of various configurations of rotating MFR configurations [6]. The goal was to determine economical alternatives to the APAR. In the future, MFRSIM will be used to study adaptive techniques using MFRs for different missions, environments, and threats. MFRs have a high level of agility in the transmitted waveform and antenna beam steering, making it possible to change scan strategies or scheduling “on the fly”. Depending on the environment and the threat, certain waveforms should provide better performance over others. The naval AN/SPY-1 radar is a good example of an advanced MFR that can modify its waveform and beam scheduling (as well as signal processing) to adapt to different missions, environments, and threats. Current emphasis on studying littoral regions where reaction time is short, make adaptive techniques more vital. The modified simulator will be used to evaluate the impact of dynamic waveforms, random scan search on the MFR performance and to optimize the transmitted waveforms for selected scenarios. The objective is to develop adaptive algorithms to continuously optimize the performance of naval radars in the ever-changing environment. The real problem is how to implement the logic to apply adaptability to the MFRs. This problem is tackled in collaboration with TTCP SEN TP 3 “Radar systems”, which is developing an adaptive MFR simulator. The intention is to develop intelligent controller of an MFR through advanced algorithms and processing. MFRSIM is one of the key components considered for the development of this adaptive MFR simulator. The intention is to use the modified simulator to optimize the parameters of the transmitted waveforms and beam scheduling as a function of the threat and environment. The results of the studies should provide a basic reference for the development of potential schemes for future MFRs. MFRSIM will also be used to evaluate the impact of sensor integration and various “hand-off” techniques and cued search methods for the overall air defence system. This research will be done in support of the Technology Demonstration Project on “Shipborne Integration Sensors and Weapons Systems” (SISWS). Sensor integration, as suggested by the NAAWS study, is also becoming much more important to win back reaction time. Presently, the modeled MFR in MFRSIM can be cued from adjunct radars such as a volume search radar. New models are required for additional sensors such as infrared search and track sensors or ESM systems (such as CANEWS 2). MFRs are able to look in any direction almost instantaneously. An attractive “hand-off” technique would use this capability to confirm detections by adjunct radars immediately rather than to wait confirmation from these slow rotating radars. Eventually, MFRSIM will be further modified to evaluate other concepts such the Cooperative Engagement Capability, and netcentric multiplatform naval surveillance. The simulation architecture of MFRSIM has been designed to allow for such modifications.


7. References

1. Martin, R., Toulgoat, M., (Jan 1997), Multifunction Radars in the NAAWS tradition, Defence Research Establishment Ottawa, report No. 1309

2. Lightstone L. Vrzic S., (Nov. 1992), Simulation Studies for the horizon search performance of a multifunction array radar, Atlantis scientific systems group inc., Contract number W7714-2-0043, Report number 288

3. Lightstone L., Vrzic S., (March 1993), Further Simulation Studies for the horizon search performance of a multi function array radar, Atlantis Scientific systems group inc, contract number W7714-2-0078, Report number 302

4. Lightstone L., Enweani B., (March 1995), New simulation studies for the horizon search performance of a multi function radar in a clutter environment, Atlantis scientific systems group inc., Contract number W7714-4-0162, Report number 363

5. Jones, G., and al, (March 1999), Rotating Multifunction Radar System Simulation and Analysis, VolI and II, Sicom systems Ltd, Contract number W7714-7-0104/001/SV

6. Jones, G., and al, (24 July 1994), Optimisation of multifunction radars through simulation, Vol I: Software, experiment descriptions and unclassified simulation results, Sicom System ltd, Contract number W7714-8-0220, Sept 200Lok J.J, Canada and Germany Join Dutch for APAR, Jane’s Defence Weekly, pp8

7. Jones, G., and al, (Sept. 2000), Optimisation of multifunction radars through simulation, Vol II: Software, experiment descriptions and classified simulation results, Sicom System ltd, Contract number W7714-8-0220

8. Jones, G., and al, (March 2001), Modification to an adaptive Multifunction radar simulator, Sicom systems Ltd, Contract number W7714-000370/001/SV

9. Jones, G., and al, (March 2002) Dynamic Multifunction radar simulator, Sicom systems Ltd, Contract number W7714-000370/001/SV

10. Lok J.J., (21 Aug 1993), Contract Signing Advances APAR, Jane’s Defence Weekly, pp21-22

11. Hobson S, (18 July 1992), APAR builds a cleaner image, Jane’s Defence Weekly, 18July 1992, pp17-18 Lok, J.J., Joint Naval Radar Project Planned, Jane’s Defence Weekly, pp14-18

12. Radar Systems Forecast, (1998), Forecast International


13. Lok J.J, (24 July 1993), Canada and Germany Join Dutch for APAR, Jane’s Defence Weekly, pp8

14. Lok J.J., (21 Aug 1993), Contract Signing Advances APAR, Jane’s Defence Weekly, pp21-22

15. PCPEM, (1990), User Guide, Signal Science Limited

16. Hosbon S, (Jan 1999), Dutch contract pushes APAR radar sales to seven while Canada waits in the wings, Jane’s International Review, pp7

17. (1998), TERPEM User Guide, Signal Science Ltd

18. Reilly, J.P., (June 1998), Clutter Models for shipboard radar application, NATO AAW System Program Office naval sea Command

19. Huizing, A.G., Theil, A., (1993) Carpet Computer-aided radar performance evaluation tool: User’s Manual, Artech House

20. Barrios, (Jan 1994), A terrain parabolic equation model for propagation in the troposphere, IEEE Transactions on Antennas and propagation, Vol. 42, No. 1

21. Craig, K.H., Levy, M.F., (Apr 1991), Paraboloic Equatiuon modelling of the effects of multipath and ducting on radar systems, IEE Proceedings-F, Vol. 138, No. 2

22. Skolnik, M. I., (1991), Radar handbook 2nd Edition, McGraw-Hill, Toronto

23. Neuvy, J., (July 1970), An aspect of determining the range of the radar detection, IEEE Trans Aero Elec Syst, Vol AES-6, No 4

24. Marcum, J.I., (Apr 1960), A statistical theory of targets detection by pulsed radar, IRE Trans information Theory, Vol IT-6, pp59-145

25. Swerling, P., (Apr 1960), Probability of detection of fluctuating targets, IRE Trans information Theory, VOL IT-6, pp269-308

26. Bird, J.S., (Nov 1985), Low Doppler target detection in ground clutter, CRC report No 13977


List of symbols/abbreviations/acronyms/initialisms

ASM Anti-Ship Missiles

APAR Active Phased Array Radar

CPF Canadian Patrol Frigate

DFB Digital Filter Bank

DND Department of National Defence

DTED Digital terrain Elevation Data

MFARSIM Multifunction Array Radar Simulator

MFR Multifunction Radar

MFRSIM Multifunction Radar Simulator

MHMT Multiple Hypothesis Multiple Targets

MTI Moving Target Indicator

PRF Pulse Repetition Frequency

RMFARSIM Rotating Multifunction Array Radar Simulator

VSR Volume Search Radar

RMF Radar Multifonction

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Defence R&D Canada - Ottawa Ottawa, ON, K1A 0Z4

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Multifunction Radar Simulator (U)

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Gauthier Sylvain, Riseborough Edwin, Nohara Tim, Jones Graeme

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December 2002

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DRDC Ottawa TM 2002-165

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(U)DRDC Ottawa has developed a multifunction radar (MFR) software simulator through several contracts with Atlantis Scientific Inc and Sicom System Ltd. The latest version, called MFRSIM, is coded in MATLAB version 6.1 (by MathWorks Inc), running on an IBM compatible PC. MFRSIM has been developed specifically to evaluate the detection capability of an MFR against anti-ship missiles (ASM) operating in a littoral environment. Both rotating and non-rotating phased array MFRs can be simulated, as well as conventional rotating antennas such as volume search radars. The simulation can include land clutter, sea clutter, chaff clutter, rain and angel clutter with jamming. MFRSIM produces detection outputs in a causal manner one dwell at a time. The detector outputs are sent to the beam scheduler and the associated tracker. The tracker automatically initiates new tracks and interfaces with the beam scheduler to request tracking dwells. The beam scheduler controls the scheduling of surveillance; confirmation, cued search and tracking beams based on the tracker and detector outputs. The parameters of the transmitted dwell can be changed from dwell to dwell. A procedure has been developed to automatically select the waveform parameters for the scheduled dwell from a library of predefined waveforms. Although this capability is still rudimentary, the intention is eventually to select a given waveform as a function of the radar returns and environmental data. (U) This report describes the major capabilities, features, models and applications of MFRSIM. The first section provides background information on the development of the DRDC Ottawa simulator over the years. The second section provides an overview of MFRSIM, including its modules and capabilities. The radar models of MFRSIM are examined in detail in the third section. This will provide a clear understanding of the capabilities and limitations of this simulator. The fourth section explains how to use MFRSIM and shows some examples. The last section discusses the future R&D plans for development of MFRSIM and its main applications.

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Multifunction Radar Simulator (MFRSIM) - Defence …cradpdf.drdc-rddc.gc.ca/PDFS/unc03/p518679.pdfDRDC Ottawa TM 2002-165 iii Executive summary In 1993, Canada, Netherlands and Germany