SOFTWARE DEFINED RADAR AND WAVEFORMS FOR STUDYING MICRO-DOPPLER SIGNATURES Baokun Liu*, Rachel Chen Ancortek Inc., Fairfax, VA, USA 22030 ABSTRACT In this paper, we investigate the use of software defined radar (SDR) to analyze the micro-Doppler signatures. The first SDR we use is based on the Universal Software Radio Peripheral (USRP) and GNU Radio, and another SDR which has several operation modes is based on field-programmable gate arrays (FPGA). Typically, the USRP-based SDR is not optimized for radar applications due to its narrow bandwidth and time-varying additional delay caused by USRP components and operating system. The FPGA-based SDR is more suitable for applications where high-resolution range information is required. Our studies indicate that both of the SDR systems are capable of producing the micro-Doppler signatures. System design challenges and measurement results will be discussed in detail. Keywords: software-defined radar, FPGA, frequency modulated continuous wave (FMCW), micro-Doppler, reconfigurable radar
1. INTRODUCTION Software-defined radar (SDR) utilizes software protocols to substitute hardware components and offers a great deal on system compactness and flexibility. The core of the SDR is the software processor using field-programmable gate array (FPGA) and/or digital signal processor (DSP). Because its programmability and re-configurability, without modifying hardware, the SDR can be adopted in different scenarios and change its operation modes, waveforms, frequency bandwidths, and processing modes. SDR needs very limited number of hardware components and thus features light weight, low power, and low cost. The inherent flexibility of the SDR makes it amenable to implementation as a multimode and multipurpose radar. Radar micro-Doppler signature allows to recognize target’s identity through its movements [1]. The micro-Doppler signatures of human gaiting have been studied over decades. As observed from radar returns from a walking human, its micro-Doppler signature shows Doppler shift of the human body and micro-Doppler shifts of the swinging arms and legs. The Doppler shift of one arm is higher and the other is lower than the body’s Doppler frequency shift. Meanwhile, the body’s Doppler shift is almost constant with a saw-tooth shape but the arm and leg’s micro-Doppler shifts are timevarying periodic curves. These unique characteristics in such detailed signatures are helpful in recognizing various human activities. Another interesting micro-Doppler signature is that from helicopter rotor blades, which has been studied for a long time [2-5]. The rotor blade is modeled as a rigid, homogeneous and linear rod rotating about a fixed axis with a constant rotational rate. From the time-frequency domain micro-Doppler signature, the number of blades, the length of the blade, the rotation rate of the rotor, and the velocity of the rotor blade tip can be estimated. These estimated features can be used to identify the type of the helicopter. The use of the SDR with FPGA to extract radar micro-Doppler signatures has been studied [6-8]. The FPGA offers the best performance for signal processing with the advantages of small size, light weight, and low power. In this paper, we further investigate the development of SDR with FPGA to analyze micro-Doppler signatures generated by targets’ micro motions. To extract micro-Doppler signature by radar, the continuous waveform (CW) radar is good enough. However, wide-band frequency modulated CW (FMCW) waveform, which is usually used to measure both the high-resolution range and Doppler information, are more useful in analyzing radar micro-Doppler signatures [9,10]. Therefore, we develop SDR with both CW and FMCW to study micro-Doppler signatures of human gaiting as well as helicopter rotor blades.
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[email protected]; phone 1 405 568-1881; www.ancortek.com
2. USRP-BASED SDR FOR MICRO-DOPPLER SIGNATURE STUDIES 2.1 Software defined CW radar system design The analysis of micro-Doppler effect does not necessarily require range information, so CW radar is good enough to produce the micro-Doppler signatures. We have developed a software defined CW (SDCW) radar system using the Universal Software Radio Peripheral (USRP) N210, GNU Radio, and host computer systems. Fig. 1 shows the high level block diagram of a 4.3 GHz SDCW radar and delineates which portion of the processing occur in which part of the system. The USRP together with a daughter-board SBX becomes a transceiver used to construct a SDR. The host computer is installed with the GNU Radio toolkit and interfaces with the USRP using a Gigabit Ethernet. The GNU Radio is used to generate constant values to the USRP and save the samples from the USRP into a file, while USRP works solely as a transmitter and receiver by providing a carrier frequency. Then, the data from GNU Radio is processed in MATLAB.
Fig. 1. Block diagram of SDCW radar
The GNU Radio flow-graph of the SDCW radar works by transmitting a 4.3 GHz carrier through the transmit chain of the USRP at a rate of 200 kilo-samples per second (ksps), as shown in Fig. 2. The down-converted received data stream from the USRP is then optionally decimated to a sampling rate that could prevent Doppler fold-over (Doppler aliasing). Several seconds worth of data is stored into a file for post-processing in MATLAB.
Fig. 2. GNU Radio flow-graph for SDCW Radar
2.2 Experiment using the SDCW radar The first experiment involves a human approaching and receding from the radar platform. Fig. 3(a) shows the microDoppler signatures of indoor human walking with no-arms swings. The joint time-frequency transform based on a simple short-time Fourier transform (STFT) is used to generate the signatures [1]. As expected, approaching the radar during the time interval 0-2.5 sec registers positive Doppler while receding from the radar during the time interval 4-6.5 sec
registers negative Doppler. Furthermore, the leg swings result in positive micro-Doppler for an approaching human and negative micro-Doppler for a receding human. The human is walking at a pace of about 1 m/s, and this corresponds to a Doppler shift of 28.7 Hz, which agrees well with the returned Doppler frequency due to torso motion. In the second experiment, a scale model helicopter is used, which has two rotor blades with a length of 0.3 m. Fig. 3(b) depicts the micro-Doppler signature of the rotating rotor blades, where 11 flashes from the blades can be seen clearly. The helicopter has no translational motion, so the Doppler shift of the fuselage should be around zero Doppler. As expected, the two blades generate a symmetric Doppler pattern around the zero Doppler. From the number of flashes in 2 seconds, the rotation rate of the rotor is estimated to be 2.75 revolution/second (r/s) (or 2.75×2π rad/sec) [1]. Thus the tip velocity of the rotor is about 5.19 m/s and the maximum Doppler shift should be about 149 Hz as shown in Fig. 3(b). 200
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Fig. 3. SDCW experiment. (a) Human walking. (b) Helicopter.
2.3 Software defined FMCW radar system design In [11], Patton proposed a Barker code SDR based on GNU Radio and USRP, where a loop-back between the transmission port and the reception port is used to calibrate the system. This calibration has to be performed for each run, and requires manually disconnecting and reconnecting the antennas. Due to time-varying additional delay caused by the USRP components and the operating system which disturbs target delay [12], the SDR can be out of calibration after running a certain time during each run. In [13], a FMCW radar based on GNU Radio and USRP was proposed for weather surveillance application. It provides an interesting concept in the system design, but the above mentioned timevarying additional delay was still not compensated. As a matter of fact, removing the effect of additional delay is the most crucial step for accurate range and Doppler measurements. In our study, we use the GNU Radio/USRP to demonstrate the capability of developing an FMCW radar and compensating the time-varying additional delay using an automatic calibration scheme based on frequency domain matched filter. The Gigabit Ethernet interface allows streaming capability up to 25 mega samples per second (msps) shared between TX and RX. The chirp signal bandwidth should be no larger than 80%-85% of 12.5 MHz when a USRP is used as a transceiver. Thus, the bandwidth of the USRP is limited to 10MHz, and the range resolution would be 15 meters. The bandwidth is an unavoidable bottleneck in using USRP to design cost-effective SDR with wide-band waveforms. Fig. 4 shows the high level block diagram of the software-defined FMCW (SDFMCW) radar with a center frequency of 4.3 GHz and a bandwidth of 10 MHz. The SDFMCW radar utilizes the same hardware as the SDCW radar. The system begins with a linear up-chirp with a period of 0.8192 ms that is generated in MATLAB. The pulse repetition frequency (PRF) is 1.221 kHz which could measure a maximum velocity of 21.3 m/s at the carrier frequency of 4.3 GHz. The chirp is then streamed into the USRP by GNU Radio and repeats continuously from the transmitter. The down-converted return echo signal is streamed into GNU Radio and mixed with the transmitted baseband chirp signal to generate the beat signal that contains range and Doppler information. The beat signal is then saved into a file for further processing in MATLAB.
Fig. 4. Block diagram of SDFMCW radar
The GNU Radio flow-graph of the SDFMCW radar begins with reading the binary file that contains the user-defined baseband signal, as shown in Fig. 5. It should be noted that this baseband signal could also be generated in GNU Radio using a saw-tooth waveform to drive a complex voltage controlled oscillator (VCO). The baseband samples are then streamed to the USRP to be up-converted and radiated at the scene by the transmit antenna. At the same time, USRP receives the echo from targets and streams the down-converted samples into GNU Radio.
Fig. 5. GNU Radio flow-graph for SDFMCW Radar
In an ideal FMCW radar system, the received signal due to a stationary target is simply a time-delayed version of the transmitted signal and will be multiplied with the transmitted one to generate the beat signal, from which the time delay and target range could be derived [13]. Unfortunately, the time-varying additional delay will disturb the delay caused by target, which would severely degrade the ability of the system to give accurate range measurements, and can indeed render the system totally ineffective when the USRP works at the bandwidth of 10 MHz. The so-called loop-back configuration proposed in [11] is used to determine the additional delay which will be removed later. A 30 dB attenuator is used to protect the SBX from physical damage. As displayed in Fig. 6(a), the beat signal is ideally centered at zero frequency in the loop-back configuration at the very beginning when the predetermined additional delay is removed. However, the peak will quickly jump around randomly as the additional delay has random and time variance properties, as shown in Fig. 6(b). Thus, one time calibration proposed in [11,12] is not enough to ensure the system is well calibrated at any later time.
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Fig. 6. Random and time variance property. (a) Calibrated. (b) Out of calibration.
In order to remove the time-varying additional delay, we propose an automatic calibration scheme based on frequency domain matched filter as shown in Fig. 7. About half second worth of samples from the beginning are dumped to remove the start-up noise from the USRP. A set of samples equal to the size of one chirp period are then collected from the transmitter and receiver. By taking FFT, performing a complex conjugate multiplication and then inverse FFT, the index of the maximum point is found in the magnitude result. This index, read by the Function Probe block at every second, indicates the real-time additional delay between the TX and RX streams and drives the Variable Delay block in the receive chain. Thus, the TX and RX streams are always aligned by removing the additional delay frequently enough. This calibration scheme is based on the assumption that there is strong direct coupling between the two directional antennas and the direct-path return is the strongest source. This is well supported in our setup since the antennas are quite close. Once the direct-path return is calibrated to the first range bin, the additional delay is removed. It should be noted that the calibration flow graph would be equivalent to the loop-back calibration method if the Limit block is enabled for only one time calibration.
Fig. 7. Calibration flow-graph for SDFMCW radar
To ensure only calibrated samples can pass to the multiplier that generates the beat signal, another half second worth of samples are dumped. The samples after complex conjugate multiplication are downsampled, and then stored into a file for post-processing in MATLAB, as shown in Fig. 8.
Fig. 8. Data recording flow-graph for SDFMCW radar
2.4 Experiment using the SDFMCW radar Several experiments are conducted to verify the operation and accuracy of the SDFMCW radar. The first experiment is to validate the automatic calibration scheme using a loop-back configuration. Shown in Fig. 9(a), the Max Index which indicates the additional delay is found, and the beat signal is always centered at zero frequency, inferrring the system is operating correctly and calibrated for range measurement. The second experiment investigates the micro-Doppler signature of a human walking backward and forward with twoarms swings. It’s observed in Fig. 9(b) that the Doppler shift of one arm is higher and the other is lower than the body’s Doppler frequency shift. Meanwhile, the body’s Doppler shift is almost constant with a saw-tooth shape but the arm’s micro-Doppler shifts are illustrated in periodic features. As expected, the torso results in a stronger return power as the human is closer to the radar platform. In the third experiment, the SDFMCW radar is used to sense a scale model helicopter rotating rotor blades. Each of the two blades has a length of 0.3 m. The helicopter has no translational motion, so the Doppler shift of the fuselage should be around zero Doppler. As displayed in Fig. 9(c), the rotor with two blades results in a symmetric Doppler pattern around the zero Doppler and 9 flashes in two seconds, indicating a rotation rate of approximately 2.25 r/s. Consequently, the tip velocity of the rotor is about 4.24 m/s and the theoretical maximum Doppler shift is about 121 Hz. This agrees well with the measured maximum Doppler shift, inferring the SDFMCW radar is operating correctly and calibrated for Doppler. The main advantage of FMCW radar over CW radar is its ability to provide range information. The fourth experiment is carried out to locate and detect a human receding from the radar platform at a pace of about 0.9 m/s. As the person is about 5 m away, a certern number of pulses are recorded and reshaped into a matrix. Fast Fourier transform (FFT) is performed in both range (fast-time) and Doppler (slow-time) dimensions to generate the range-Doppler image. Displayed in Fig.9(d), the human is registered at approaximately 0.9 m/s and 5 m. It’s worth to note that the 3 dB range is about 20 m. This range matches well with the theoretical range resolution of 19.5 (1.3×15) m. As a hamming window is used before taking FFTs, a slight sacrifice (≈1.3) in range resolution is incurred [14]. 0
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Fig. 9. SDFMCW experiment. (a) Calibration in loop-back mode. (b) Human walking. (c) Helicopter. (d) Range-Doppler.
3. PROPOSED SDR FOR MICRO-DOPPLER SIGNATURE STUDIES 3.1 Reconfigurable SDR based on FPGA Zhang, et al. proposes a SDR system that combines both FMCW and pseudorandom (PN) pulse radar functions for automotive applications [15]. DDS is used to generate different baseband signals, while DSP is used to process the received signals. FMCW signal with a bandwidth of 300 MHz could be provided, but the use of laboratory instruments, such as oscilloscope and signal generator, makes it far from a real cost-effective compact radar prototype. Starting from the above consideration, we propose a reconfigurable compact radar which supports CW, FMCWSawtooth, FMCW-Triangle, and Frequency Shift Keying (FSK) operation modes. Fig. 10 shows the high level block diagram of the proposed 5.8 GHz SDR. A graphical user interface (GUI) written in MATLAB provides a simple interface for users to control the SDR configuration via a USB 2.0 cable and to view the real-time results of signal processing. Digital samples of control voltage are generated by the FPGA firmware, and then converted to analog control voltage which directly feeds the VCO. It’s worth to note that the nonlinearity in frequency modulation of the VCO could be compensated by generating a predetermined control voltage in FPGA. The return echo signal will be mixed with the transmitted signal to generate the beat signal which will be digitized and streamed to MATLAB for real-time processing in m-code.
Fig. 10. Block diagram of compact SDR
The bandwidth of the beat signal is much smaller than that of the baseband signal for short-range FMCW radars, so sampling the beat signal instead of the baseband signal will ease the sampling burden on ADC and streaming burden on computer-radar interface. Thus, the bandwidth of FMCW signal could be set up to more than 400 MHz for the proposed SDR, and a range resolution of 0.375 m could be achieved. 3.2 FPGA-based SDR in CW mode The SDR could be easily adapted for working in different modes. The following two experiments are performed when the SDR works in CW mode with a sampling rate of 128 kHz. The first experiment entails a human running away from the radar platform at a pace of about 3 m/s. As shown in Fig. 11(a), the human is registered at approximately 125 Hz, indicating a velocity of 3.23 m/s. Compared to the micro-Doppler signature of a walking person in Fig. 9(b), the microDoppler signature of a running person has a short gait cycle due to quick arms movements. It’s interesting to note that the body’s Doppler shift is almost constant with a saw-tooth shape. Fig. 11(b) shows the micro-Doppler signature of a two-blade rotor on a scale model helicopter measured by the 5.8 GHz SDR working in CW mode. The helicopter has no translational motion, so the Doppler shift of the fuselage should be around zero Doppler. Rotation rate of the rotor is about 2.5 r/s and the blade length is 0.3 m. Thus the tip velocity of the rotor is about 4.71 m/s and the theoretical maximum Doppler shift is about 182 Hz. As observed from Fig. 11(b), the measured maximum Doppler shift is approximately 180 Hz, inferring the SDR working in CW mode is capable of accurate Doppler measurements. 0
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Fig. 11. Compact SDR in CW mode. (a) Human running. (b) Helicopter.
3.3 FPGA-based SDR in FMCW mode The SDR is configured to work in FMCW mode with a bandwidth of 400 MHz for the following two experiments. The chirp sweep period is chosen to be 1 ms. The PRF is 1 kHz which could measure a maximum velocity of 12.9 m/s at the carrier frequency of 5.8 GHz. In the first experiment, the SDR is used to detect a human running away from the radar with hands carrying load. The sampling rate is chosen to be 1024 kHz to accommodate the large bandwidth of the beat signal due to human running at a far range. As expected in Fig. 12(a), the legs movements result in peaks in periodic features while no arms movements are detected. As the human is farther from the radar platform, the power of the echo due to the torso becomes weaker, and the micro-Doppler signatures due to legs motions become invisible. Fig. 12(b) shows the micro-Doppler signature of a two-blade rotor on a scale model helicopter measured by the 5.8 GHz SDR working in FMCW mode with a sampling rate of 128 kHz. The helicopter has no translational motion, so the Doppler shift of the fuselage should be around zero Doppler. 8 flashes from the blades can be seen clearly, indicating the rotation rate of the rotor is about 2 r/s. The blade length is 0.3 m, so the tip velocity of the rotor is about 3.77 m/s and the theoretical maximum Doppler shift is about 146 Hz. This agrees well with the measured maximum Doppler shift in Fig. 12(b), inferring the system is operating correctly.
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Fig. 12. Compact SDR in FMCW mode. (a) Human running. (b) Helicopter.
4. CONCLUSION Two SDR prototypes for studying micro-Doppler signatures are present here: a USRP-based SDR using GNU Radio as the software toolkit and a FPGA-based SDR using MATLAB as the signal processing tool. The main challenges in developing USRP-based SDR are the narrow bandwidth which fundamentally affects range resolution and the timevarying additional delay which disturbs accurate measurements. Meanwhile, the FPGA-based SDR provides a much wider bandwidth and a user-friendly MATLAB interface. Both of the SDRs allow accurate Doppler measurements for human gait recognition and helicopter classification.
ACKNOWLEDGMENT The authors would like to thank Chris Gianelli and David J. Greene of Integrated Adaptive Applications, Inc. for the valuable discussions on calibration procedures of FMCW radar. The authors would also like to thank Dr. V. C. Chen and Dr. Z. Lu of Ancortek Inc. for valuable suggestions and discussions.
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[9] M de Wit, J. J., Harmanny, R. I. A., and Premel-Cabic, G., "Micro-Doppler analysis of small UAVs," Proc. EuRAD, pp. 210-213, (2012). [10] Vignaud, L., Ghaleb, A., Kernec, J. L. and Nicolas, J. M., "Radar high resolution range and micro-Doppler analysis of human motions," RadarCon, Bordeaux, (2009). [11] Patton, L. K., "A GNU Radio Based Software Defined Radar," Master Thesis, Department of Electrical Engineering, Wright State University, Utah, (2007). [12] Prasetiadi, A. E. and Suksmono, A. B., "A simple delay compensation system in software-defined Frequency Modulated Continuous (FMCW) Radar," Eur. Wireless, Poland, (2012). [13] Prabaswara, A., Munir, A., Suksmono, A. B., "GNU Radio based software-defined FMCW radar for weather surveillance application," Proc. Int. Conf. on TSSA, 227-230, (2011). [14] Brooker, G., [Sensors and Signals], University of Sydney, (2006). [15] Zhang, H., Li, L., and Wu, K., "24GHz software-defined radar system for automotive applications," Proc. Eur. Conf. on Wireless Technol., 138-141, (2007).
