Drone Detection and RCS Measurements with Ubiquitous Radar

Álvaro Duque de Quevedo, Fernando Ibañez Urzaiz, Javier Gismero Menoyo, Alberto Asensio López Information Processing and Telecommunications Center. Universidad Politécnica de Madrid

Madrid, Spain aduque@gmr.ssr.upm.es, f.ibanez@upm.es, javier@gmr.ssr.upm.es, vera@gmr.ssr.upm.es

Abstract— This paper presents experimental results on commercial-drone detection with a ubiquitous frequency modulated continuous wave (FMCW) radar system, working at 8.75 GHz (X-band). The system and its main blocks are briefly introduced. Subsequently, the document presents the chosen scenario for the tests, and shows the results of the offline signal processing, achieving a DJI-Phantom-4 detection at a range up to 2 km. The results are illustrated with range-Doppler matrices and detection figures. Range, speed and azimuth accuracies are discussed, considering the drone GPS data. Finally, the paper introduces a statistical radar-cross-section (RCS) study based on the processed data, in order to classify the drone as a Swerling target, and discusses the drone average RCS.

Keywords— drone detection; persistent radar; radar cross-sections; radar detection; radar remote sensing; RPA detection; UAV detection; ubiquitous radar.

I. INTRODUCTION

We are currently living a boom in drone use, for not only defense purposes but also commercial, professional, and entertainment. This rise is based on the great commercial offer, which is constantly growing, the relatively affordable cost of drones, its effectiveness in surveillance tasks and in package delivering, and the inherent allure of the easy handling of this cutting-edge technology. Drones provide a new form of entertainment for everyone with amazing multimedia results. Maybe, one day, seeing drones will be as normal as seeing mail trucks on the road [1], or any toy at home.

However, this quick evolution also implies the rise of a new threat to global security from a number of points of view. On the one hand, the right to privacy of individuals may be easily compromised, and their security could be put at risk because of the proximity of these aircrafts in flight. Furthermore, drones represent a potential risk of plane crash when passing through protected airspace or flying near airports. Finally, the same mentioned usability and the easy access to these systems may result in an advantage for terrorists, who can commit their attacks in a more effective and less exposed way.

Hence, on a par with this drone boom, the competent authorities have rushed to upgrade the attendant legislation and many systems for drone detection and shoot-down have arisen. Amongst all of them (e.g. audio- or video-based systems), radar systems are positioning themselves one step ahead because of their inherent advantages (e.g. its independence

from weather or lighting conditions, and their suitability for the operational needs in drone detection [2]). In spite of this, the detection of a commercial micro-UAV is a real challenge for radar technology due mainly to its small size and plastic-made and low-reflective materials translating to its very low Radar Cross Section (RCS) and its ability to fly slowly at ground level, making its echoes to compete with high clutter signals.

This paper introduces our RAD-DAR: a small, quick-to-deploy and low-powered radar demonstrator system, based on the ubiquitous radar concept [3], which is able to detect and track a small commercial drone flying at a range of 2 km. Our first experimental results, introduced in [4], showed the feasibility of achieving strong range-speed association with the developed demonstrator. This article presents the results of new tests (Sec. V) focused on drones instead of cars, with very promising outcomes. Collected data have been also employed in order to study the drone RCS, as will be seen in Sec. VI.

II. X-BAND UBIQUITOUS RADAR: SYSTEM DESCRIPTION

A. Ubiquitous radar concept

The system presented in this article is a ubiquitous radar demonstrator [3], which always stares at the whole surveillance scene with a wide transmitted beam. An 8-channel digital array receives the echoes, digitizes, and stores them for further processing. The system uses multiple simultaneous beams generated on reception by means of digital beamforming, in order to achieve the required azimuthal coverage.

The absence of scanning (mechanical or electronic) allows for reaching an optimal trade-off between the dwell time and the refreshing rate of target data. This enables the system to adapt in order to detect and track low radar cross section targets with slow dynamics (e.g. drones), without degrading abilities to detect other faster and bigger targets (e.g. vehicles, planes).

RAD-DAR

Tx beamSynthesized Rx beams

Fig. 1 Ubiquitous Radar working principle.

B. Hardware description

Fig. 2 shows a diagram of the radar demonstrator. The RAD-DAR employs a programmable signal generator to obtain a frequency modulated continuous wave (FMCW), on a frequency band centered at 8.75 GHz (X-band) with a maximum bandwidth of 500 MHz, and clock and trigger signals. A transmitter amplifies the signal up to 5W and generates the Local Oscillator (LO) sample for subsequent demodulation. The 8 receiving antennas and the transmitting one are designed with microstrip technology [5].

C. Software description

The signal from 8 receivers is acquired by a commercial 8-channel digitizer [6] connected to a computer by a PCIe port. A Matlab script has been developed to carry out the digitizer control. This Signal Acquisition Subsystem captures data arranging them into cubes (Fig. 3) for a posterior 3D processing. These three dimensions are range (the system captures L samples per trigger), time (N ramps are acquired) and azimuth (M channels, M = 8).

The block diagram in Fig. 3 is a quick description of the Radar Software, including the Acquisition Subsystem, the Radar Signal Processor, and the Data Processor.

The offline Radar Signal Processor, implemented with Matlab, works with the data cubes from the Acquisition Subsystem. It performs a 3D processing (Two-dimension Fast Fourier Transform, beamforming and monopulse) and a detection and plot extraction stage.

1) Two-dimension Fast Fourier Transform (2D FFT) By means of a FMCW waveform [4], target range

information lies in the “beating frequency” (fb), which is the difference between transmitted and reflected frequencies. A Fast Fourier Transform (FFT) is applied to the L dimension (see Fig. 3) in order to obtain the beating frequency, and so the range information. A second FFT, performed over the N dimension, provides Doppler information (speed) if care is taken to ensure the synchronism during the acquisition process.

2) Beamforming and Monopulse On the basis of the signals coming from the 8 channels, the

system synthesizes 5 reception beams corresponding to

pointing angles θ between -40º and +40º (see Fig. 4). To do this, a phase increment is applied to each received signal, according to the inter-element spacing and the pointing angle desired [7], [4], [5]. By adding the 8 phase-shifted signals, range-Doppler matrices are generated for each pointing angle.

In order to improve the azimuth accuracy, the Signal Processor performs a monopulse technique. After generating the sum signal at the beamforming process, a difference signal is also obtained for each pointing angle, θ, by subtracting channel 5-8 signals from channel 1-4 signals. The quotient between the magnitudes of difference signal (∆) and sum signal (Σ) yields the Monopulse Function [8, Sec. 9.2]. The azimuth accuracy improvement is achieved by comparing the Monopulse Function values obtained from signal processing, using data cubes, with the corresponding Monopulse Function values obtained from anechoic-chamber data [8], [4].

3) Decision and plot extraction A Cell-Averaging Constant False Alarm Rate (CA-CFAR)

technique [7] is applied at detection stage in order to obtain a binary cube with detections. This is carried out by comparing the signal power at a range bin with an adaptive threshold calculated as the average signal power of its adjacent cells (reference cells). Each target detection is turned to a plot (a single-point detection) by computing its center of mass, usually referred to as centroid [9]. Thus, after detection, a list of plots is obtained, each one containing information about range, speed, azimuth, received signal power and time.

A Data Processor, which is currently under development, obtains tracks from plots which belong to a tracked target.

Tx Antenna Rx Antenna, 8 Receivers

RAD – DAR

Transmitter

LO

8 ch DigitizerPCIe Express

PC(Signal Processor, Data Processor)

ClockTrigger

8.75GHzPower supply

Signal Generator

Fig. 2 Block diagram of the RAD-DAR Hardware.

ACQUISITION SUBSYSTEM OFFLINE RADAR SIGNAL PROCESSOR

8-channel Digitizer

Control &AcquisitionSoftware 1st FFT Lwin 2nd FFT Nwin

Beamforming M&

Mononopulsewin

CA – CFAR

DetectionT

Plot extraction

Trakingalgorithms

OFF-LINE RADAR DATA PROCESSOR

Fig. 3 Block diagram of the RAD-DAR Software, and data cube.

Fig. 4 RAD-DAR synthesized beams by Beamforming.

1 N

slow-time, ramps (Doppler, speed)

1

M

channels(azimuth)

fast-time, samples (range)

1

L

III. DRONE DETECTION TEST SETUP

A. Target Characteristics

The tests described in this paper were performed with a commercial micro-drone (also referred to as micro- Unmanned Aerial Vehicle, micro-UAV, or micro- Remotely Piloted Aircraft, micro-RPA), DJI Phantom 4 [10], designed for both private and professional use. Table I shows its main features.

The main material of the drone body is plastic, the propellers are made with glass fiber reinforced composite and and the four rotors are made of carbon fiber and plastic as well. A considerable amount of literature has been published on drones cross section. Refs [11], [12] and [13] show results on RCS obtained by means of anechoic chamber measurements or simulation, of a DJI Phantom 2, which is similar to Phantom 4 in terms of weight, size, materials and shape. There is a view widely held in these articles that a Phantom drone may be modeled as a Swerling 1 target (SW1) with an average RCS of approximately 0.01m2 (-20 dBsm).

This drone is able to record its telemetry data during a flight. These data (e.g. GPS coordinates, range, speed) are collected at 10 Hz sample frequency and easy to export for further processing.

B. Field-test scenario

The selected scenario for the field tests is a farm located at a village in the province of Ávila, Spain. Its geographical coordinates are 40°49'47.3"N 4°48'00.4"W [14]. The system was installed over an emplacement with entirely unobstructed line of sight extended up to 5 km.

As Fig. 5 shows, the drone described a round trip for these tests. The outward flight was driven by a pilot, by means of the manual mode of the drone remote control. The return flight was carried out in automatic “Go to home” mode. The maximum range achieved by the drone was 2 km. Table II and Table III summarize the data of these flights.

In view of these flight data, it is easy to conclude that the return flight is the best to assess the radar performance, since drone managed to keep a quasi-constant trajectory with low speed deviation by means of that automatic control mode. Thus, Section IV will focus in that radar results of the return flight, although the outward flight will be also introduced.

Fig. 6 shows the radar system installed for the test. There were some birds of prey at the coverage area, which sometimes appeared in the captured data. Although the studied data for this paper are focused on the drone flight, future articles will deal with bird detection, tracking, and micro-doppler studies.

C. Radar setup

The performed drone-detection experiments were carried out with a radar setup summarized by Table IV, Table V, and Table VI, introducing waveform, operative and acquisition parameters.

As can be seen in these tables, it can be expected that the system achieves a strong range-speed association due to the corresponding resolutions. The dwell time of 0.17s allows the radar to reach a high speed resolution and an enough integration time to raise the Signal-to-Noise Ratio (SNR), thus achieving the desired detection range. The maximum unambiguous range and the Doppler ambiguity seem to be

TABLE I. DJI PHANTOM 4 MAIN SPECIFICATIONS

Weight (propellers and battery included) 1380 g Diagonal length (propellers not included) 350 mm Maximum speed 20 m/s (72 km/h) Maximum flying height (above sea level) 6000 m Positioning system GPS/Glonass Maximum flying time (battery life) 28 minutes Maximum range (remote control range) 5 km

TABLE II. FIELD TESTS: DRONE OUTWARD FLIGHT

Flight Control Mode Manual Flight time 271 s Average speed 8.26 m/s (29.72 km/h) Standard deviation of the speed 0.57 m/s (2.04 km/h) Average altitude (over the floor) 21.6 m Standard deviation of the altitude 0.1 m

TABLE III. FIELD TESTS: DRONE RETURN FLIGHT

Flight Control Mode Auto (Go to Home) Flight time 237 s Average speed 9.96 m/s (35.85 km/h) Standard deviation of the speed 0.06 m/s (0.22 km/h) Average altitude (above ground level) 30.2 m Standard deviation of the altitude 0.2 m

Fig. 5 Field-test scenario. Fig. 6 RAD-DAR and drone, at site for field test

sufficient in order to accomplish the early warning of drones [2]. Finally, the 400ms-gap between cubes ensures an appropriate refreshment rate for the tracking algorithms.

It is important to note that this configuration leads to a raw-data rate of almost 3 Gbps, which real-time processing will imply a great technical challenge in future work stages.

IV. THEORETICAL ANALYSIS OF MICRO-UAV DETECTION

On the basis of the system-and-target parameters introduced in Section III, a number of theoretical studies, based on the Radar Range Equation [7], have been carried out to evaluate the radar performance in terms of target detection probability. Fig. 7 represents the Probability of Detection, Pd, for a target modeled as Swerling 1, with RCS = 0.02 m2, and False Alarm Probability, Pfa, of 10-3, 10-6 and 10-9 respectively.

Fig. 7 predicts the potential detection capability for a drone at a range of 2 km if False Alarm Probability is high enough. Although this will lead to a considerable number of false alarms, tracking algorithms will extract the drone plots from noise and will clean the scene.

V. DRONE DETECTION RESULTS

The drone was detected and tracked from the beginning to the end of the flights, achieving a detection-range up to 2 km, with Pd > 0.7, as was predicted in Section IV.

A. Linear target path (return flight)

Fig. 8 shows a range-Doppler matrix with raw data (after 2D FFT and beamforming) with pointing angle θ = 0º, where clutter can be appreciated at zero Doppler along every range bins. When zooming (Fig. 9) the drone can be found at 0.83km-range and 36km/h-speed bins. This figure corresponds to the cube number 200/400 of the return flight test (negative drone speed because it is approaching to the radar system).

Fig. 10 shows the detected drone speed vs distance. Average detected speed was 35.95 km/h with a standard deviation (STD) of 0.19 km/h, which is similar to the GPS data (Table III). The average-speed error, comparing radar measurements with GPS data, is 0.0068 km/h.

Fig. 11 shows from both radar processed data and drone GPS data. That figure enables us to see the strong range-speed association achieved, even with the naked eye. Root-mean-square deviation (RMSE) has been computed between GPS and radar data, resulting in 0.20 km/h.

Fig. 12 shows the drone azimuth vs distance, where it can be seen a detected average azimuth angle of -2.15º against the radar pointing angle θ = 0º, with a STD of 0.7º. It has to be noticed that when the drone is far away, the azimuth deviation is higher because this measurement accuracy depends on the monopulse function, which in turn depends on the received power level from the drone echoes, lower when the drone is far. It is also easy to notice that when the drone is approaching

TABLE IV. WAVEFORM PARAMETERS

Radar frequency (f1) 8.75 GHz Bandwidth (Δf) 200 MHz Ramp period (Tm) 350 μs Ramp frequency (fm) 2.86 kHz

TABLE V. UBIQUITOUS RADAR PERFORMANCE

Range Resolution (ΔR) 0.878 m Maximum unambiguous range (Rmna) 3598 m (fb = 16 MHz) Doppler Resolution 5.58 Hz, 0.096 m/s, 0.34 km/h Doppler ambiguity 1.4 KHz, 24.5 m/s, 88.1 km/h

TABLE VI. ACQUISITION PARAMETERS

Number of samples per ramp (L) 8192 Number of integrated ramps (N) 512 Number of channels (M) 8 Number of range bins (nBins) 4096 (3598 m) Dwell time 0.1792 s Number of synthesized beams 5 Sample rate (fs) 32 MHz Maximum beating frequency (fbmax) 16 MHz (R = 3598 m) Number of cubes per acquisition 400 Time gap between cubes 400 ms Total scene time per acquisition 231.28 s

Range-doppler matrix after beamformingSum Diagram = 0º

-80 -60 -40 -20 0 20 40 60 80speed (km/h)

0

0.5

1

1.5

2

2.5

3

3.5-200

-180

-160

-140

-120

-100

-80

-60

-40

Fig. 8 Range-Doppler Matrix. Raw data

Ran

ge (k

m)

dBm

Fig. 9 Range-Doppler Matrix. Zoom to the drone

Fig. 7 Pd vs Range

the radar, its average azimuth angle against the radar normal is not as constant as when the drone is far. This occurs because the initial and last points of the drone trajectory are not exactly the radar coordinates (it took off from a place 5-7 m far away from the radar). When the drone “goes to home”, it gets back to the same point where it took off. If azimuth is computed only for the second third of the flight, average and STD measured azimuth are -2.02º and 0.53º respectively.

In the light of these outcomes, it is clear that tracking algorithms will be based on range-speed association, and azimuth data will be filtered to improve the radar performance.

Fig. 13 shows the evolution of received peak power from drone echoes, in dBm, vs distance, where it can be noticed the effect of target range in the received power, and the effect of the receiver transfer function [5], which tends to reduce the power of near target echoes. These data are employed in order to compute the drone RCS, as will be exposed in Section VI.

Future works will study the use of frequency agility in order to circumvent signal fading. This can be achieved by dividing the received data-cubes into smaller portions (along L-dimension, see Fig. 3) before 2D FFT. Each small cube will correspond to a ramp center-frequency different from the others, thus creating frequency-agile waveforms from a single original ramp. Then, independent FFTs are applied to each small cube and the resulting complex cubes are summed, yielding to a new cube (with less range-bins, and larger ones, than the original cube) ready to continue the processing.

B. Non-linear target path (outward flight)

Finally, this section presents a quick view of the outward flight results in Fig. 14, which shows both radar and GPS data of speed vs range relationship, with non-linear trajectory because of the manual control of the drone. As can be seen in Fig. 14, the pilot did not keep a constant drone speed during this flight, thus providing a very good way to see, with the naked eye, the range-speed target association achieved with the radar demonstrator. The RMSE of the measured speed data here, compared with the GPS data, is 0.24 km/h.

Fig. 10 Detected speed vs range.

Fig. 11 Speed (radar and GPS) vs range.

Fig. 12 Azimuth vs range.

Fig. 13 Power vs range.

Fig. 14 Speed vs range. Outward flight

VI. DRONE RADAR CROSS-SECTION

Fig. 15 shows the computed drone-RCS vs distance, with an average RCS of 0.02 m2, which was to be expected from Sec. III.A. These RCS data show a scan-to-scan decorrelation, and an exponential Probability Density Function (PDF), which enables us to classify our drone as a SW1 target [7].

Fig. 16 shows both a histogram with RCS data from Fig 15, and a chi-square PDF with two degrees of freedom (i.e. SW1 exponential PDF). The similarity between theoretical and empirical PDFs is clear, even though the data set has only less than 400 plots. Indeed, the χ2 goodness-of-fit test did not rejected the null hypothesis at the 5% significance level with a p-value of 0.27.

VII. CONCLUSIONS AND FURTHER WORK

This paper has presented our RAD-DAR (a small, quick-to-deploy and low-powered radar demonstrator system, based on the persistent radar concept) in a drone detection operation. The results presented here have shown the RAD-DAR capability to detect a micro-UAV at a range of 2 km with an excellent range-speed association.

The collected data have served, furthermore, to study the distribution of the drone echoes power, in order to prove that

DJI-Phantom-4 echoes actually show the statistical behavior of SW1 targets with average RCS near 0.01 m2.

New range-test have been carried out with very promising outcomes, achieving drone detection up to 3.2 km. Other tests have been also performed, including drone flights over trees, to highlight the system ability to fight against clutter. These tests will be covered in future articles that also will discuss target classification (drones vs birds), micro-doppler studying, and tracking filters implementation.

ACKNOWLEDGMENT

The authors would like to thank the Spanish Comisión Interministerial de Ciencia y Tecnología (CICYT, project TEC2014-53815-R, RAD-DAR) for partially finance this research work, and Advanced Radar Technologies (ART) for support the field tests and provide their facilities.

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Fig. 15 measured RCS vs range.

Fig. 16 SW1 PDF of drone echoes.