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Signal processing structure for automotive radar* By Florian Fölster, Hermann Rohling Abstract – Automotive radar sensors will be used in many future applications to increase comfort and safety. Compared to classical radar applications like air surveillance, the automotive radar observation area is rather small, but will contain numerous different targets. Due to the close distance of these objects the target resolu-tion procedures become very important. This paper gives an overview about automotive radar signal process-ing schemes in multiple target situations. Index Terms – Automotive radar, data association, signal processing, multi target environment

1. Introduction

Radar sensor based driver assistance systems for automotive applications are currently under investigation to increase drivers comfort and safety. For adaptive cruise control (ACC) applications, a single 77GHz far distance radar sensor is used which has a long maximum range of 200m and covers a narrow azimuth angle of 14 degree for example. All targets inside the observation area will be detected and the measured range between the relevant object and the host vehicle will be used to control the distance between the two cars.

For many additional automotive applications like Stop&Go, Pre-Crash or Parking Aid, a completely different observation area is needed. In this case a maximum range of 50m but a wide azimuth angle area of ±70 degree is required. For these demanding applications a single radar sensor is often not sufficient but radar networks consisting of several short range sensors are considered to achieve the desired coverage area as shown in Figure 1.

The objective of this paper is to discuss the general signal processing design for typical automotive radar applications. Advantages and disadvantages of different system procedures and techniques are described and compared especially in multiple target situations.

Fig. 1: Automotive radar observation area.

2. Automotive Radar sensor systems

Automotive radar based ACC functionality is the first appli-cation that is commercially available in mass-production vehicles of almost all car manufactures. The ADR system from Volkswagen and the Distronic system of Mercedes-Benz are only two examples. The aim of these systems is mainly to increase the comfort of the driver [1].

Active-safety systems based on radar sensors are currently under investigation for lane-change assistants, pedestrian safety, collision warning or collision avoidance [2, 3] respec-tively. For these short range applications radar networks have been developed [4] to cover the required safety area.

In all automotive radar applications the dense and multi-target situation is a characteristic feature. Furthermore, almost all automotive radar objects cannot be considered as point targets any longer, but must be processed as extended targets. This is a real technical challenge to resolve all targets by simultaneous range, Doppler-frequency and azimuth angle measurement.

Simultaneous range and velocity measurements are mainly influenced by the choice of radar waveform which can be optimized in accordance with the range and velocity resolu-tion requirements.

The azimuth angle measurement performance is mainly in-dependent of the radar waveform, but will be influenced by the antenna design using e.g. beam-forming or monopulse techniques. Table 1 gives an overview about the relevant radar system design parameters for far-distance (ACC) and near-distance setups.

Table 1: System requirements.

ACC Near distance application System

Term Single

sensor Multilateration Single sensor

Maximum range 200m 50m 50m

Range resolution 1m 1m 1m

Range accuracy 0.3m 0.02m 0.3m

Maximum angle ±7° ±70° ±70°

Angular accuracy 0.5° (2°) 2°

Maximum velocity 150m/s 30m/s 30m/s

Velocity resolution 1m/s 0.2m/s 0.2m/s

Velocity accuracy 0.3m/s 0.06m/s 0.06m/s

Time on target

2ms @ 77GHz

30ms @ 24GHz

30ms @ 24GHz

* This work was supported by Hamburg University of Technology TUHH.

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2.1 Angular measurement based on a single sensor

As already mentioned, ACC systems are based on a single radar sensor setup. This sensor must be able to detect the most relevant targets inside a highway scenario and estimate their respective position and movements. Therefore, a simultane-ous measurement of target range, azimuth angle and radial velocity is necessary.

The target movement is assumed to be mainly characterized by a strait moving path with a high velocity and large dis-tances between adjacent targets. In this motorway scenario the radar target type is limited to vehicles and trucks only.

Since only a single sensor is available for data collection, this sensor must be capable to measure azimuth angle for the necessary lane assignment of the observed vehicles on the highway. Usually monopulse or sequential-lobbing tech-niques are applied for this purpose.

2.2 Angular measurement based on a radar network

Besides single sensor configurations, also sensor networks are discussed and will become very important for automotive radar applications. The reason for using sensor networks could be manifold. In addition to an increased observation area, a sensor network can alternatively estimate the target position based on multilateration procedures. In this case, each sensor has to measure the target range extremely accu-rate.

Since radar networks are mainly considered for near dis-tance applications with a maximum distance up to 50m, the sensor networks have to deal with the typical near distance situations. The first kind of a near distance application is the improvement of the ACC function to a Stop&Go procedure where the sensor should guide the host car to a complete stop and subsequent to a restart maneuver later.

One common feature of all near distance applications is the high target density inside the limited observation area. This is a typical case for traffic jam situations on motorways or typical dense city traffic. Furthermore, target characteristics like physical extensions or specious reflections from moving target parts have to be considered within the radar signal processing design. In these different street and traffic scenar-ios the radar target type can be vehicles, pedestrians bicycle driver etc.

There was a long debate between automotive radar experts to consider multilateration techniques or to use alternatively monopulse procedures. In this respect the possible ambigui-ties in the radar measurement play an important role. Many techniques to resolve ambiguities in range and/or velocity are well known. In any case where ambiguities cannot be re-solved ghost targets will occur. The multilateration technique is a specific procedure to resolve angular ambiguities. For that reason monopulse technique will have some advantages to avoid any ambiguities in the azimuth angle measurement.

3. Radar signal processing chain

The general signal processing architecture for automotive radar sensors can be split into different functional parts. In the first stage of the radar signal processing design a matched filter is implemented to maximize the signal-to-noise ratio for a given waveform. The following target detection scheme can be considered as a second stage of the processing chain. In the third step an unambiguous range and velocity measurement will be processed which is mainly influenced by the wave-form design. The azimuth angle measurement is the fourth step to estimate the target position and a tracking procedure is the fifth step of the radar signal processing chain.

Figure 2 gives an overview of the complete signal process-ing chain as a block diagram. Besides the different steps of radar signal processing three target association blocks have been included into the block diagram. These data association stages are essential if ambiguities will occur in the radar measurement process. Some of these ambiguities depend on the respective system and waveform design like the range-velocity ambiguity in case of a linear frequency modulated waveform (LFMCW), but others like target-to-track associa-tion in multi-target scenarios are inherent to the radar signal processing system. It is well known that the data association is one of the most crucial parts of multi-target tracking sys-tems [5]. Since dense multi-target situations are characteristic for automotive radar applications, any extra ambiguities within the system design have to be avoided.

Fig. 2: Automotive radar signal processing chain.

3.1 Detection

As already mentioned in the previous section, the target envi-ronment for automotive radar applications differs from other radar applications like e.g. air surveillance. Automotive sys-tems have to handle especially multi target situations in clut-ter environment. So-called clutter reflections are caused by the reflectivity of the road surface and all surrounding items like bushes or buildings.

Target detection schemes are based on an ordered-statistic constant-false-alarm-rate processing (OS-CFAR) which have been shown to give the best results in analytical calculations and practical measurements in multi target situation [6].

3.2 Simultaneous range and velocity measurement

The intention of automotive radar waveform design is the simultaneous measurement of target range and velocity. Up to now, all different kinds of classical radar waveforms like pulse, pulse-Doppler, continuous wave (CW), frequency shift keying (FSK) or frequency modulated continuous wave (FMCW) have also been considered for automotive related applications [1, 4, 7-9].

Typical design parameters are a range resolution of around 1m, which results in a necessary bandwidth of 150MHz and a velocity resolution of 0.2m/s, which demands on a time on target of about 30ms, considering a carrier frequency of 24GHz.

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Since multi-target situations almost always occur in auto-motive radar applications the question of target separation and waveform design related ambiguities is essential. To fulfill the desired task of simultaneous range and velocity measure-ments, different waveform techniques will be considered. The pulse-Doppler signal processing scheme, for example, consists of no range-velocity ambiguities while the LFMCW waveform almost always leads to range-velocity ambiguities. In this case specific techniques to resolve these ambiguities are essential. One commonly used procedure is the introduc-tion of additional redundant measurements, like it is per-formed in the four-chirp LFMCW waveform [1]. While just two chirps with different chirp rates are necessary to resolve the range-velocity ambiguity in a single target situation, two extra chirps have been introduced for an unambiguous meas-urement in multiple target scenarios.

As a consequence a data association scheme is needed to find the corresponding detections for a single target. Fig. 3 visualizes the two different versions of signal processing with (1) or without (2) the additional data association step. Espe-cially in multiple target situations or even a single but ex-tended target situation, the data association procedure be-comes an important topic. In all these cases a high risk of ghost target generation is given. Examples for CW transmit signals with unambiguous range-velocity measurement are for example linear modulated frequency shift keying (LFMSK) [10, 11] waveform designs. This waveform is a combination of FSK and LMFCW measurement principles which is de-scribed in detail in [1, 7]. FSK has the main advantage of an extremely high velocity resolution and unambiguous velocity measurement procedure. But there is no range resolution possible with pure FSK. LFMCW is characterized by an ambiguous range and velocity measurement.

Fig. 3: Handling ambiguities in range-velocity measurement.

3.3 Azimuth angle estimation

Beside the measurement of target range and velocity also the knowledge of the target azimuth angle is of great importance. Mechanical rotating antennas as used in military and naval applications are obviously not feasible for automotive radar applications.

To measure the target angle different techniques have been proposed and developed: ─ Multiple parallel beams [3] ─ Digital beamforming techniques [12] ─ Electronically scanning antenna beams [13] ─ Sequential lobing techniques [9] ─ Monopulse antenna systems [14] and ─ Multilateration techniques [4, 8]

In present sensor networks multilateration techniques have

been used for position estimation, while single sensor systems were utilizing monopulse techniques or sequential lobing [15, 16]. For the multilateration case each sensor has a large beam width and no angular measurement capability. Therefore, every single sensor measurement produces ambiguities in target position estimation. Similar to the explanations for

resolving range-velocity ambiguities by means of an extended waveform, the position estimation based on multilateration techniques requires more than two radar sensors. Again an additional data association step is required inside the signal processing chain as depicted by path (1) in Fig. 4. Following the abovementioned explanations this additional data associa-tion step results in the risk of ghost target generation in case of multiple or extended target situations and should therefore be avoided, path (2) in Fig. 4. Present radar network systems use monopulse techniques for this purpose.

Fig. 4: Handling ambiguities in target position measurement.

3.4 Target tracking

As already mentioned above a target tracking over time is always needed in a radar system to increase reliability, over-come detection leaks, improve the estimation accuracy and enable a later situation analysis.

To handle different models of target motion and sensor measurement characteristics even in dense and multiple target situations, lots of techniques for target tracking procedures have been developed and published [5]. Adaptations of these systems to the specific automotive conditions are given for example in the recent publications of [8, 17, 18].

Automotive targets have a very limited speed of less then 75m/s for highway and 15m/s for city traffic scenarios in connection with a maximal target acceleration of 1m/s2. Compared with the standard update rate of greater 30Hz, the motion of observed targets is predictable with high accuracy.

The data association procedure is inherent for all tracking procedures and will be very important in all multi-target situations. Fig. 5 shows some different ways of assigning the measured data to already established tracks. The simplest way, path (3) in the figure, is to directly compare the esti-mated target position, which is the output of the position estimation step, with the estimated target position based on the track prediction.

More reliable ways of track updating procedures utilize the information of already established tracks to calculate directly the output of one of the preceding signal processing steps and directly associate the respective data to the tracks. In case of a multilateration scheme the measured target ranges could be directly used to update the tracks [15], path (2) in Fig. 5, or if this network consists of LFMCW radar sensors the detected beat frequencies could be used for the track update [8], path (1) in Figure 5. With this kind of signal processing technique the number of necessary data association steps will be re-duced to a single one.

Fig. 5: Strategies for existing track update.

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4. Experimental measurement results

To demonstrate the typical conditions for range and velocity data in near distance applications, Fig. 6 shows measurements of these values for a car passing by on the neighboring lane with decreasing distance. The measurements are given as small circles, while the expected target length is given as a straight line in the upper part of the diagram.

Fig. 6: Range and velocity measurements over the time for an extended target in a city traffic scenario.

Form the figure it is obvious that the singe extended target

causes multiple reflections within one measurement step. These multiple and closely spaced measurements have to be handled correctly by the signal processing scheme to avoid any ghost targets in such an extended target situation. Besides multiple reflections in the range measurements, the velocity spreads over a certain interval with decreasing distance given by the different aspect angles of the respective centers of reflection. To resolve these effects an unambiguous range and velocity measurement is indispensable.

5. Summary

Based on a general radar signal processing chain, the automo-tive radar specific topics in the design of radar systems have been introduced and compared. An important objective in radar system design is to avoid any ambiguities in radar signal measurement especially in multiple target situations. Any error in the association step will lead to ghost targets, which reduces the tracker and system performance. For that reason monopulse techniques implied in a radar network have some advantages compared with multilateration techniques to avoid any ambiguities in the data association step.

In addition to the used angular estimation technique, also an unambiguous waveform design should be considered for simultaneous range and velocity measurements without any extra data association step.

References

[1] H. Rohling, M.-M. Meinecke and R. Mende, “A 77GHz Automotive Radar System for AICC Applications”, MIKON 98, 1998

[2] M.-M. Meinecke, M.-A Obojski, M. Töns and M. Dehesa: “SAVE-U: First experiences with a pre-crash system for enhancing pedes-trian safety”, ITS-Europe 2005, 2005

[3] U. Haberland: “Multi-Beam Automotive Radar”, IRS 2005, pp. 619–621, 2005

[4] M. Klotz, “An Automotive Short Range High Resolution Pulse Radar Network”, PhD, TU Hamburg-Harburg, 2002

[5] S. Blackman and R. Popoli, “Design and Analysis of Modern Track-ing Systems”, Boston: Artech House, 1999

[6] H. Rohling, “Radar CFAR Thresholding in Clutter and Multiple Target Situations”, IEEE Trans. Aerosp. Electron. Syst. vol. 19, no. 4, pp. 608-621, 1983

[7] J. Klauder, A. Price, S. Darlington. and W. Albersheim: “The Theory and Design of Chirp Radars”, Bell System Technical Jour-nal, vol. 39, no. 4, pp. 745-808, 1960

[8] U. Lübbert, “Target Position Estimation with a Continuouse Wave Radar Network”, PhD, TU Hamburg-Harburg, 2005

[9] H. Henftling, D. Klotzbücher and, C. Frank: “Ultra Wide Band 24GHz Sequential Lobig Radar for Automotive Applications”, IRS 2005, pp. 79–82, 2005

[10] M.-M. Meinecke and H. Rohling, “Combination of LFMCW and FSK Modulation principles for Automotive Radar”, Proc. German Radar Symposium, 2000

[11] H. Rohling, F. Fölster, M.-M. Meinecke and R. Mende, “A New Generation of Automotive Radar Waveform Design Techniques”, Proc. First International Conference on Waveform Diversity and Desgin, 2004

[12] K. Schuler, R. Lenz and W. Wiesbeck: “Digital Beam-Forming for Automotive SRR”, IRS 2005, pp. 613–617, 2005

[13] U. Meis and R. Schneider: “Information Retrieval Capabilities of High-Resolution Mobile Radar”, WIT 2005, pp. 101-105, 2005

[14] R. Mende, M. Behrens, M.-M. Meinecke et.al.: “The UMRR-S: A High-Performance 24GHz Multi-Mode Automotive Radar Sensor for Comfort and Safety Applications”, IRS 2003, pp. 113–118, 2003

[15] M. Schiementz, F. Fölster and H. Rohling: “Angle Estimation Techniques for different 24GHz Radar Networks”, IRS 2003, pp. 405–410, 2003

[16] D. Oprisan, F. Fölster and H. Rohling: “Monopulse versus Multilat-eration Technique for Azimuth Measurement in Automotive Radar Networks”, IRS 2005, pp. 73–78, 2005

[17] D. Oprisan and H. Rohling, “Tracking Systems for Automotive Radar Networks”, IEE Radar 2002, 2002

[18] M. Lee and Y. Kim: “New data association method for automotive radar tracking”, IEE Prc.-Radar, Sonar Navig, no.148, pp. 297-301, 2001

Florian Fölster Hermann Rohling Department of Telecommunications Hamburg University of Technology (TUHH) 21073 Hamburg Germany Fax: +49–40–42878–2281 e-mail: f.foelster@tu-harburg.de; rohling@tu-harburg.de

(Received on December 13, 2005)

(Revised on Deember 13, 2005)