SPWVD with AdaptiveFilterBankSupported RF Sensor for Low RCS Targets’ Nonlinear MicroDoppler Signature/Pattern Imaging System
Abstract
:1. Introduction
2. Mathematical Descriptions
2.1. ShortTerm Fourier Transform
2.2. Wavelet Transform
2.3. Smoothed PseudoWigner–Ville Distribution with an Adaptive Filter Bank
3. Design for RF Sensor and Experiments
 (a)
 Acquisition of RF sensor signal/data samples at 10 kHz: raw nonstationary multifrequency data/samples having the targets’ behavioral microDoppler signature/pattern profiles;
 (b)
 Plotting the time series data of the RF sensor;
 (c)
 (d)
 Computation and plotting of the power spectrum of sensor data using Equation (1);
 (e)
 Extracting the main features from the power spectrum of sensor data (from the results of step (d) to decide/design the adaptive decomposition filter bank structure;
 (f)
 Decomposition of signals. The response of the LPF is divided into two bands: 0–0.5 KHz using an LPF designed based on Equation (13) and 0.5–1 KHz using a bandpass filter (BPF) designed as follows [35,43];$${H}_{BPF}(\omega )=\{\begin{array}{c}1\mathrm{if}{\omega}_{l}\le \omega \le {\omega}_{h}\\ 0\mathrm{otherwise}\end{array}$$
 (g)
 Sublevel decomposition of the sensor signal: the required levels, within the Doppler band, are obtained using enough LPFs and BPFs, i.e., the decomposition filter bank structure;
 (h)
 Responses of all LPFs and BPFs are linearly amplified using a constant gain of 20 dB;
 (i)
 Amplified signals are passed through the SPWVD algorithm (Equation (12)), and all computations are performed, in parallel, using parloop in the MATLAB environment;
 (j)
 Summing the results of all SPWVD channels: sum = { SPWVD (LPF_{1}) + SPWVD (LPF_{2}) + …..+ SPWVD (LPF_{x}) } + { SPWVD(BPF_{1})+ SPWVD(BPF_{2}) +…….+ SPWVD (BPF_{y}) }, where $x\in 1,2,\dots .H;y\in 1,2,\dots ..I$; the values of H and I are integers and depend on the Doppler bandwidth;
 (k)
 MicroDoppler signature/pattern imaging based on the results of the summed SPWVD.
Algorithm 1. Pseudo Code: Proposed SPWVD with an adaptive filter bank 

4. Results and Data Analysis
4.1. Experimental MicroDoppler Signature/Pattern Generation Accuracy Comparative Analysis
4.2. Recognition of the MicroDoppler Signature/Pattern of Two Targets’ Motions: Rotational Propeller System and Flapping Bionic Bird
4.3. Extraction of MicroDoppler Signature/Pattern Profiles of Three Targets’ Motions: Three and TwoBlade Rotational Propeller Systems and a Flapping Bird
4.4. Signature/Pattern Imaging of a Static Rotational (Propeller System) and a Guided Orbital Motion Target
4.5. Behavioral Imaging of Activities of Three Different Targets: ThreeBlade Propeller Rotation System, Kinetic Warhead Motion, and Bird’s Flapping Action
5. StateoftheArt (SOTA) Comparative Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
 Skolnik, M.I. Introduction to Radar System, 3rd ed.; McGraw Hill Education: New York, NY, USA, 2001. [Google Scholar]
 Gite, T.Y.; Pradeep, P.G.; Raj, A.A.B. Design and Evaluation of CBand FMCW Radar System. In Proceedings of the 2nd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 11–12 May 2018; IEEE: New York, NY, USA, 2018; pp. 1274–1276. [Google Scholar]
 Anan, A.P.L.; Kumawat, H.C.; Raj, A.B. Moving Target Detection in Foliage Environment using FMCW Radar. In Proceedings of the 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 10–12 June 2020; IEEE: New York, NY, USA, 2020; pp. 418–421. [Google Scholar]
 Molchanov, P.; Harmanny, R.I.; De Wit, J.J.; Egiazarian, K.; Astola, J. Classification of small UAVs and birds by microDoppler signatures. Int. J. Microw. Wirel. Technol. 2014, 6, 435–444. [Google Scholar] [CrossRef] [Green Version]
 Rahman, S.; Robertson, D.A. Radar microDoppler signatures of drones and birds at Kband and Wband. Sci. Rep. 2018, 8, 1–11. [Google Scholar] [CrossRef] [PubMed]
 Farshchian, M.; Selesnick, I.; Parekh, A. Bird body and wingbeat radar Doppler signature separation using sparse optimization. In Proceedings of the 24th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Aachen, Germany, 19–22 September 2016; IEEE: New York, NY, USA, 2016; pp. 71–74. [Google Scholar]
 Behera, D.K.; Raj, A.B. Drone Detection and Classification using Deep Learning. In Proceedings of the 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 13–15 May 2020; IEEE: New York, NY, USA, 2020; pp. 1012–1016. [Google Scholar]
 Chen, V.C. The MicroDoppler Effect in Radar, 1st ed.; Artech House: Norwood, MA, USA, 2011. [Google Scholar]
 Rajkumar, C.; Raj, A.B. Design and Development of DSP Interfaces and Algorithm for FMCW Radar Altimeter. In Proceedings of the 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India, 17–18 May 2019; IEEE: New York, NY, USA, 2019; pp. 720–725. [Google Scholar]
 Mazumder, J.; Raj, A.B. Detection and Classification of UAV Using Propeller Doppler Profiles for Counter UAV Systems. In Proceedings of the 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 10–12 June 2020; IEEE: New York, NY, USA, 2020; pp. 221–227. [Google Scholar]
 Garg, U.; Raj, A.A.B.; Ray, K.P. Cognitive Radar Assisted Target Tracking: A Study. In Proceedings of the 3rd International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 15–16 October 2018; IEEE: New York, NY, USA, 2018; pp. 427–430. [Google Scholar]
 Gong, J.; Yan, J.; Li, D.; Kong, D.; Hu, H. Interference of radar detection of drones by birds. Prog. Electromagn. Res. M 2019, 81, 1–11. [Google Scholar] [CrossRef] [Green Version]
 Jian, M.; Lu, Z.; Chen, V.C. Experimental study on radar microDoppler signatures of unmanned aerial vehicles. In Proceedings of the 2017 IEEE Radar Conference (RadarConf), Seattle, WA, USA, 8–12 May 2017; IEEE: New York, NY, USA, 2017; pp. 854–857. [Google Scholar]
 Ritchie, M.; Fioranelli, F.; Borrion, H.; Griffiths, H. Multistatic microDoppler radar feature extraction for classification of unloaded/loaded microdrones. IET Radar Sonar Navig. 2017, 11, 116–124. [Google Scholar] [CrossRef] [Green Version]
 Hui, Y.; Bai, X.; Zhou, F. JTF Analysis of Micromotion Targets Based on SingleWindow Variational Inference. IEEE Trans. Geosci. Remote Sens. 2021, 59, 6600–6608. [Google Scholar] [CrossRef]
 Rivera Suana, J.A. Design of a LowNoise Amplifier for Radar Application in the 5 GHz Frequency Band. 2017. Available online: http://www.divaportal.org/smash/get/diva2:1115519/FULLTEXT01.pdf (accessed on 20 October 2021).
 Chen, V.; Li, F.; Ho, S.S.; Wechsler, H. Analysis of microDoppler signatures. IEE Proc. Radar Sonar Navig. 2003, 150, 271. [Google Scholar] [CrossRef]
 Shakya, P.; Raj, A.A.B. Inverse Synthetic Aperture Radar Imaging Using Fourier Transform Technique. In Proceedings of the 1st International Conference on Innovations in Information and Communication Technology (ICIICT), Chennai, India, 25–26 April 2019; IEEE: New York, NY, USA, 2019; pp. 1–4. [Google Scholar]
 Gupta, D.; Raj, A.A.B.; Kulkarni, A. MultiBit Digital Receiver Design for Radar Signature Estimation. In Proceedings of the 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 18–19 May 2018; IEEE: New York, NY, USA, 2018; pp. 1072–1075. [Google Scholar]
 Charvat, G.L. Small and ShortRange Radar Systems; CRC Press: Boca Raton, FL, USA, 2014. [Google Scholar]
 Su, B.Y.; Ho, K.C.; Rantz, M.J.; Skubic, M. Doppler Radar Fall Activity Detection Using the Wavelet Transform. IEEE Trans. Biomed. Eng. 2015, 62, 865–875. [Google Scholar] [CrossRef]
 Meena, P.; Sharma, R.R.; Pachori, R.B. Crossterm suppression in the WignerVille distribution using variational mode decomposition. In Proceedings of the 5th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 10–12 October 2019; IEEE: New York, NY, USA, 2019; pp. 323–328. [Google Scholar]
 Whitelonis, N.; Ling, H. Radar Signature Analysis Using a Joint TimeFrequency Distribution Based on Compressed Sensing. IEEE Trans. Antennas Propag. 2013, 62, 755–763. [Google Scholar] [CrossRef]
 Wang, X.J.; Xue, Y.J.; Zhou, W.; Luo, J.S. Spectral Decomposition of Seismic Data with Variational Mode DecompositionBased Wigner–Ville Distribution. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 4672–4683. [Google Scholar] [CrossRef]
 Klaer, P.; Huang, A.; Sévigny, P.; Rajan, S.; Pant, S.; Patnaik, P.; Balaji, B. An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions. Sensors 2020, 20, 5940. [Google Scholar] [CrossRef]
 Xiao, Z.; Yan, Z. Radar Emitter Identification Based on Novel TimeFrequency Spectrum and Convolutional Neural Network. IEEE Comm. Lett. 2021, 25, 2634–2638. [Google Scholar] [CrossRef]
 Zhou, Y.; Bi, D.; Shen, A.; Wang, X. Hough transformbased large micromotion target detection and estimation in synthetic aperture radar. IET Radar Sonar Navig. 2019, 13, 558–565. [Google Scholar] [CrossRef]
 de Quevedo, Á.D.; Urzaiz, F.I.; Menoyo, J.G.; López, A.A. Drone detection and radarcrosssection measurements by RADDAR. IET Radar Sonar Navig. 2019, 13, 1437–1447. [Google Scholar] [CrossRef]
 Kumawat, H.C.; Raj, A.B. Extraction of Doppler signature of microtomacro rotations/motions using continuous wave radar—Assisted measurement system. IET Sci. Meas. Technol. 2020, 14, 772–785. [Google Scholar] [CrossRef]
 Matsuda, T.; Yataka, R.; Gocho, M.; Tanaka, T. MicroDoppler Analysis under Various Aspect Angles for Small UAV Classification. In Proceedings of the 2019 IEEE AsiaPacific Microwave Conference (APMC), Singapore, 10–13 December 2019; IEEE: New York, NY, USA, 2019; pp. 102–104. [Google Scholar]
 Phanindra, B.R.; Pralhad, R.; Raj, A.B. Machine Learning Based Classification of Ducted and NonDucted Propeller Type Quadcopter. In Proceedings of the 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 6–7 March 2020; IEEE: New York, NY, USA, 2020; pp. 1296–1301. [Google Scholar]
 Ramesh, K. EBird Technology; Wildlife Institute of India: Uttarakhand, India, 2018. [Google Scholar]
 Suresh, P.; Thayaparan, T.; Obulesu, T.; Venkataramaniah, K. Extracting MicroDoppler Radar Signatures from Rotating Targets Using Fourier–Bessel Transform and Time–Frequency Analysis. IEEE Trans. Geosci. Remote Sens. 2013, 52, 3204–3210. [Google Scholar] [CrossRef]
 Kumawat, H.C.; Raj, A.B. Approaching/Receding Target Detection using CW Radar. In Proceedings of the 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 10–12 June 2020; IEEE: New York, NY, USA, 2020; pp. 136–141. [Google Scholar]
 Bazil Raj, A.A. FPGABased Embedded System Developer’s Guide, 1st ed.; CRC Press: Boca Raton, FL, USA, 2018. [Google Scholar]
 MathWorks. Spectrogram Using ShortTime Fourier Transform. Available online: https://in.mathworks.com/help/signal/ref/spectrogram.html (accessed on 20 October 2021).
 Addison, P.S. The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2017. [Google Scholar]
 Boashash, B. TimeFrequency Signal Analysis and Processing: A Comprehensive Reference; Elsevier Science: Amsterdam, The Netherlands, 2015. [Google Scholar]
 Kumawat, H.C.; Raj, A.B. Data Acquisition and Signal Processing System for CW Radar. In Proceedings of the 5th International Conference on Computing, Communication, Control And Automation (ICCUBEA), Pune, India, 19–21 September 2019; IEEE: New York, NY, USA, 2019; pp. 1–5. [Google Scholar]
 Cohen, L.; Loughlin, P.J. leID1. Timefrequency analysis: Theory and applications. J. Acoust. Soc. Am. 2013, 134, 4002. [Google Scholar] [CrossRef]
 Kalra, M.; Kumar, S.; Das, B. Moving Ground Target Detection with Seismic Signal Using Smooth Pseudo Wigner–Ville Distribution. IEEE Trans. Instrum. Meas. 2020, 69, 3896–3906. [Google Scholar] [CrossRef]
 Mahafza, B.R. Radar Signal Analysis and Processing Using MATLAB; Routledge: Boca Raton, FL, USA, 2016. [Google Scholar]
 Proakis, J.G.; Manolakis, D.G. Digital Signal Processing, 4th ed.; Pearson Education India: Chennai, India, 2007. [Google Scholar]
 Wang, Y.; Liu, Q.; Fathy, A.E. CW and Pulse–Doppler Radar Processing Based on FPGA for Human Sensing Applications. IEEE Trans. Geosci. Remote Sens. 2012, 51, 1–11. [Google Scholar] [CrossRef]
 Bazil Raj, A.A.; VijayaSelvi, J.A.; Raghavan, S. Realtime Measurement of Meteorological Parameters for Estimating Low Altitude Atmospheric Turbulence Strength (C_{n}^{2}). IET Sci. Meas. Technol. 2014, 8, 459–469. [Google Scholar] [CrossRef]
 Chen, V.C. Advances in applications of radar microDoppler signatures. In Proceedings of the 2014 IEEE Conference on Antenna Measurements & Applications (CAMA), Anitbes JuanlesPins, France, 16–19 November 2014; IEEE: New York, NY, USA, 2014; pp. 1–4. [Google Scholar]
 Vishwakarma, S.; Ram, S.S. Detection of Multiple Movers Based on Single Channel Source Separation of Their MicroDopplers. IEEE Trans. Aerosp. Electron. Syst. 2017, 54, 159–169. [Google Scholar] [CrossRef]
 Fioranelli, F.; Ritchie, M.; Griffiths, H.; Borrion, H. Classification of loaded/unloaded microdrones using multistatic radar. Electron. Lett. 2015, 51, 1813–1815. [Google Scholar] [CrossRef] [Green Version]
 Kim, B.K.; Kang, H.S.; Park, S.O. Experimental Analysis of Small Drone Polarimetry Based on MicroDoppler Signature. IEEE Geosci. Remote Sens. Lett. 2017, 14, 1670–1674. [Google Scholar] [CrossRef]
 Choi, I.O.; Kim, K.T.; Jung, J.H.; Kim, S.H.; Park, S.H. Efficient Measurement System to Investigate MicroDoppler Signature of Ballistic Missile. Int. J. Aeronaut. Space Sci. 2016, 17, 614–621. [Google Scholar] [CrossRef] [Green Version]
 Sun, H.X.; Liu, Z. Microdoppler feature extraction for ballistic missile warhead. In Proceedings of the 2008 International Conference on Information and Automation, Changsha, China, 20–23 June 2008; IEEE: New York, NY, USA, 2008; pp. 1333–1336. [Google Scholar]
 Jung, J.H.; Kim, K.T.; Kim, S.H.; Park, S.H. Microdoppler extraction and analysis of the ballistic missile using rda based on the real flight scenario. Prog. Electromagn. Res. M 2014, 37, 83–93. [Google Scholar] [CrossRef] [Green Version]
 He, F.; Wang, P.; Xu, X. Coning target micromotion feature extraction via scattering centre reconstruction. Electron. Lett. 2014, 50, 1388–1389. [Google Scholar] [CrossRef]
S. No.  Target  Specification 

1  Bionic bird (Figure 1a)  Flapping speed: 1–4 flap/s; wing length: 19 cm; bird’s body width: 5.5 cm; wing beat control: 3 V DC motor; flap control voltage: 1.9–3.0 V; control provision: pot. knob variation; RCS [42]: ~−28.86 dBsm 
2  3Blade propeller (Figure 1b)  Blade length: 7.5 cm; maximum blade width: 7 cm; blade thickness: 0.1 cm; maximum revolutions per minute (RPM): 1910; blade propulsion: 5 V DC motor; RPM control provision: pot. knob variation; RPM control voltage: 0.5–5 V; RPM measurement: using a tachometer; sensor frequency to RPM conversion: RPM = (fx60)/n [25,29]; RCS [42]: ~−0.268 dBsm. 
3  2Blade propeller (Figure 1c)  Blade length: 20 cm; maximum blade width: 4.5 cm; blade thickness: 0.1 cm; maximum RPM: 1740; blade propulsion: 230 V AC motor; RPM control provision: silicon control rectifier (SCR) knob variation; RPM control voltage: 100–230 V; RPM measurement: using a tachometer; sensor frequency to RPM conversion: RPM = (fx60)/n [25,29]; RCS [42]: ~9.77 dBsm 
4  Kinetic warhead (Figure 1d)  Height: 16 cm; base radius: 7 cm; mass: 1 kg; mounting: screw with fuel propeller system; nose alignment: 3axis tip tilt; RCS [42]: ~−41.87 dBsm. 
S. No.  Type of Target  Authors  RF Sensor  Imaging Technique  Improvements Attended Using the Proposed Sensor/Technique  Comparative Remark 

1  Flapping bird  Molchanov, P., et al. [4]  Continuouswave (CW) sensor (9.5 GHz)  STFT  Maximum microDoppler: 1 kHz Maximum flapping speed: 28 m/s Frequency resolution: ~0.5 Hz Time resolution: ~0.08 s Sensor: CW (5.3 GHz) Maximum range: 100 m Target horizontal orientation: 0°–360° Target vertical orientation: 0°–360° Target behaviors: sustained flapping/flying/gliding  Improved performance is obtained using the proposed RF sensor and technique than the SOTA results. 
2  Rahman, S., et al. [5]  CW sensor (24 GHz and 94 GHz)  
3  Farshchian, M., et al. [6]  CW sensor (24 GHz)  
4  Chen, V.C. [46]  Frequencymodulated continuouswave (FMCW) sensor (Xband)  
1  Rotational propeller system  Rahman, S., et al. [5]  CW sensor (24 GHz and 94 GHz)  STFT  Maximum microDoppler: 1 kHz Maximum rotational frequency: 1 kHz Maximum RPM: 20,000 (for 3 blades), 30,000 (for 2 blades) Frequency resolution: ~0.5 Hz Sensor: CW (5.3 GHz) Maximum range: 100 m Target vertical orientation: −90°–+90° Target behaviors: sustained flying/tilted flying  Improved performance is obtained using the proposed RF sensor and technique than the SOTA results. 
2  Vishwakarma S., et al. [47]  CW sensor (7.5 GHz)  
3  Fioranelli, F., et al. [48]  Pulsed sensor (2.4 GHz)  
4  Kim, B.K., et al. [49]  FMCW sensor (14.03–14.18 GHz)  
1  Motorized laboratory model kinetic warhead structure  Choi, I., et al. [50]  Pulsed sensor (10 GHz; PRF = 1 kHz)  STFT  Maximum microDoppler: 1 kHz Maximum angular velocity: 6.28 E3 rad/s Frequency resolution: ~0.5 Hz Sensor: CW (5.3 GHz) Maximum range: 100 m Target horizontal orientation: −45° to +45° Target vertical orientation: −45° to +45° Eclipse sensing: yes Tilted orbital path: Yes −30° to +30° Orbital path: circular or arbitrary Target behaviors: horizontal/vertical orbital motion, projectile motion, spinning  Improved performance is obtained than the SOTA results’ expected range. Since [47,48] show simulations, the target range is in the order of kilometers. 
2  Simulation model of a ballistic missile warheadstructurespinning profile  Sun, H., et al. [51]  Pulsed sensor (10 GHz; PRF = 250 Hz)  SPWVD  
3  Simulation model of a warheadstructurespinning profile  Jung, J., et al. [52]  Pulsed sensor (10 GHz; PRF = 6 kHz)  STFT  
4  Coning target model warhead structure  He, F., et al. [53]  Pulsed sensor (10 GHz; PRF = 5 Hz)  STFTbased scattering center reconstruction method 
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. 
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kumawat, H.C.; Raj, A.A.B. SPWVD with AdaptiveFilterBankSupported RF Sensor for Low RCS Targets’ Nonlinear MicroDoppler Signature/Pattern Imaging System. Sensors 2022, 22, 1186. https://doi.org/10.3390/s22031186
Kumawat HC, Raj AAB. SPWVD with AdaptiveFilterBankSupported RF Sensor for Low RCS Targets’ Nonlinear MicroDoppler Signature/Pattern Imaging System. Sensors. 2022; 22(3):1186. https://doi.org/10.3390/s22031186
Chicago/Turabian StyleKumawat, Harish C., and A Arockia Bazil Raj. 2022. "SPWVD with AdaptiveFilterBankSupported RF Sensor for Low RCS Targets’ Nonlinear MicroDoppler Signature/Pattern Imaging System" Sensors 22, no. 3: 1186. https://doi.org/10.3390/s22031186