Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming

doi:10.23919/JSEE.2020.000066

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (5): 859-870.doi: 10.23919/JSEE.2020.000066

• Electronics Technology • Next Articles

Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming

Chen TIAN(), Yang PEI*(), Peng HOU(), Qian ZHAO()

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received:2019-05-14 Online:2020-10-30 Published:2020-10-30
Contact: Yang PEI E-mail:tianchen@mail.nwpu.edu.cn;yang@nwpu.edu.c;yaphets640@mail.nwpu.edu.cn;zhaoqian1192@mail.nwpu.edu.cn
About author:TIAN Chen was born in 1992. He received his B.S. and M.S. degrees in aircraft design from Northwestern Polytechnical University in 2013 and 2016, respectively. He is currently pursuing his Ph.D. degree at Northwestern Polytechnical University. His research interests include aircraft design, aircraft susceptibility assessment, electronic countermeasures, and multi-target tracking. E-mail: tianchen@mail.nwpu.edu.cn|PEI Yang was born in 1978. He received his B.S., M.S., and Ph.D. degrees in aircraft design from Northwestern Polytechnical University in 2000, 2003, and 2006, respectively. Currently he is a professor and doctoral supervisor at Northwestern Polytechnical University. His research interests include aircraft conceptual design, target damage assessment, aircraft survivability design, as well as aircraft effectiveness and cost analysis. E-mail: peiyang yang@nwpu.edu.c|HOU Peng was born in 1991. He received his B.S. and M.S. degrees in aircraft design from Northwestern Polytechnical University in 2013 and 2016, respectively. He is currently pursuing his Ph.D. degree at Northwestern Polytechnical University. His research interests include aircraft design, target damage assessment and aircraft survivability design. E-mail: yaphets640@mail.nwpu.edu.cn|ZHAO Qian was born in 1992. She received her B.S. and M.S. degrees in aircraft design from Northwestern Polytechnical University in 2013 and 2016, respectively. Currently she is a doctoral student in aircraft design at Northwestern Polytechnical University. Her research interests include aircraft conceptual design and aircraft survivability design. E-mail: zhaoqian1192@mail.nwpu.edu.cn
Supported by:
the National Natural Science Foundation of China(11472214);This work was supported by the National Natural Science Foundation of China (11472214)

Abstract

Abstract:

Multi-range-false-target (MRFT) jamming is particularly challenging for tracking radar due to the dense clutter and the repeated multiple false targets. The conventional association-based multi-target tracking (MTT) methods suffer from high computational complexity and limited usage in the presence of MRFT jamming. In order to solve the above problems, an efficient and adaptable probability hypothesis density (PHD) filter is proposed. Based on the gating strategy, the obtained measurements are firstly classified into the generalized newborn target and the existing target measurements. The two categories of measurements are independently used in the decomposed form of the PHD filter. Meanwhile, an amplitude feature is used to suppress the dense clutter. In addition, an MRFT jamming suppression algorithm is introduced to the filter. Target amplitude information and phase quantization information are jointly used to deal with MRFT jamming and the clutter by modifying the particle weights of the generalized newborn targets. Simulations demonstrate the proposed algorithm can obtain superior correct discrimination rate of MRFT, and high-accuracy tracking performance with high computational efficiency in the presence of MRFT jamming in the dense clutter.

Key words: multi-range-false-target (MRFT) jamming, multi-target tracking (MTT), probability hypothesis density (PHD), target amplitude feature, gating strategy

Chen TIAN, Yang PEI, Peng HOU, Qian ZHAO. Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming[J]. Journal of Systems Engineering and Electronics, 2020, 31(5): 859-870.

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References 34

1	JI Z, WANG G, ZHANG X, et al. Technique of anti-multi-range-false-target jamming for radar network based on double discrimination. Proc. of the IEEE International Conference on Radar, 2017, 1- 5.
2	HUANG D T, CUI G L, YU X X, et al. Joint range-velocity deception jamming suppression for SIMO radar. IET Radar, Sonar & Navigation, 2018, 13 (1): 113- 122.
3	LU G, LEI Y, BU Y, et al. Adaptive cancellation for multiple range false radar targets. Proc. of the IEEE International Congress on Image and Signal Processing, 2014, 1535- 1539.
4	ZHAO S S, ZHANG L R, ZHOU Y, et al. Signal fusion-based algorithms to discriminate between radar targets and deception jamming in distributed multiple-radar architectures. IEEE Sensors Journal, 2015, 15 (11): 6697- 6706. doi: 10.1109/JSEN.2015.2440769
5	ABDALLA A, ABDALLA H, RAMASAN M, et al. Overview of frequency diverse array in radar ECCM applications. Proc. of the IEEE International Conference on Communication, 2017, 1- 9.
6	QUAN Y, LI Y C, WU Y, et al. Moving target detection for frequency agility radar by sparse reconstruction. Review of Scientific Instruments, 2016, 87 (9): 811- 815.
7	ZHAO S S, ZHOU Y, ZHANG L R, et al. Discrimination between radar targets and deception jamming in distributed multiple-radar architectures. IET Radar, Sonar & Navigation, 2017, 11 (7): 1124- 1131.
8	AHMED A, SHOKRALLAH A M G, YUAN Z, et al. Deceptive jamming suppression in multistatic radar based on coherent clustering. Journal of Systems Engineering and Electronics, 2018, 29 (2): 269- 277.
9	SUN D X, WANG G H, LI Y C, et al. Low observable target tracking processing in the presence of multi-range-false-target jamming. Acta Electronica Sinica, 2016, 44 (4): 826- 837.
10	LI Y C, WANG G H, SUN D X, et al. Technique against self-protection repeating track false-target deceptive jamming for radar. Systems Engineering and Electronics, 2015, 37 (6): 1242- 1248.
11	XIONG W, YIN J, ZHANG Y, et al. Trilinear decomposition-based spatial-polarisational filter method for deception jamming suppression of radar. IET Radar, Sonar & Navigation, 2016, 10 (4): 765- 773.
12	WEN C, PENG J, ZHOU Y, et al. Enhanced three-dimensional joint domain localized STAP for airborne FDA-MIMO radar under dense false-target jamming scenario. IEEE Sensors Journal, 2018, 18 (10): 4154- 4166. doi: 10.1109/JSEN.2018.2820905
13	HABTEMARIAM B, THARMARASA R, THAYAPARAN T, et al. A multiple-detection joint probabilistic data association filter. IEEE Journal of Selected Topics in Signal Processing, 2013, 7 (3): 461- 471. doi: 10.1109/JSTSP.2013.2256772
14	SI W J, WANG L W, QU Z Y. A measurement-driven adaptive probability hypothesis density filter for multitarget tracking. Chinese Journal of Aeronautics, 2015, 28 (6): 1689- 1698. doi: 10.1016/j.cja.2015.10.004
15	YANG J L, YANG L, YUAN Y, et al. Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets. Chinese Journal of Aeronautics, 2016, 29 (6): 1740- 1748. doi: 10.1016/j.cja.2016.09.010
16	SI W J, WANG L W, ZHI Q. Multi-target state extraction for the SMC-PHD filter. Sensors, 2016, 16 (6): 901- 915. doi: 10.3390/s16060901
17	MAHLER R. Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans. on Aerospace and Electronic Systems, 2003, 39 (4): 1152- 1178. doi: 10.1109/TAES.2003.1261119
18	LI T C, CORCHADO J M, SUN S, et al. Multi-EAP: extended EAP for multi-estimate extraction for SMC-PHD filter. Chinese Journal of Aeronautics, 2017, 30 (1): 368- 379. doi: 10.1016/j.cja.2016.12.025
19	LI T C, SUN S, SATTAR T P. High-speed sigma-gating SMC-PHD filter. Signal Processing, 2013, 93 (9): 2586- 2593. doi: 10.1016/j.sigpro.2013.03.011
20	CLARK D, RISTIC B, VO B N, et al. Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR. IEEE Trans. on Signal Processing, 2010, 58 (1): 26- 37.
21	ZHENG Y M, SHI Z G, LU R X, et al. An efficient data-driven particle PHD filter for multitarget tracking. IEEE Trans. on Industrial Informatics, 2013, 9 (4): 2318- 2326. doi: 10.1109/TII.2012.2228875
22	MAHLER R. PHD filters of higher order in target number. IEEE Trans. on Aerospace and Electronic Systems, 2007, 43 (4): 1523- 1543. doi: 10.1109/TAES.2007.4441756
23	REUTER S, VO B T, VO B N. The labeled multi-Bernoulli filter. IEEE Trans. on Signal Processing, 2014, 62 (12): 3246- 3260. doi: 10.1109/TSP.2014.2323064
24	VO B N, VO B T, PHUNG D. Labeled random finite sets and the Bayes multi-target tracking filter. IEEE Trans. on Signal Processing, 2014, 62 (24): 6554- 6567. doi: 10.1109/TSP.2014.2364014
25	RISTIC B, CLARK D, VO B N, et al. Adaptive target birth intensity for PHD and CPHD filters. IEEE Trans. on Aerospace and Electronic Systems, 2012, 48 (2): 1656- 1668. doi: 10.1109/TAES.2012.6178085
26	RUAN Y, WILLETT P. Multiple model PMHT and its application to the second benchmark radar tracking problem. IEEE Trans. on Aerospace and Electronic Systems, 2004, 40 (4): 1337- 1350. doi: 10.1109/TAES.2004.1386885
27	LI X H, LUO J Q, WANG W T. Adaptive detection of phase quantized DRFM deception jamming. Journal of Data Acquisition and Processing, 2015, 30 (6): 1302- 1309.
28	YUAN C S, WANG J, LEI P, et al. Multi-target tracking based on multi-bernoulli filter with amplitude for unknown clutter rate. Sensors, 2015, 15 (12): 30385- 30402. doi: 10.3390/s151229804
29	TIAN X. Radar deceptive jamming detection based on goodness-of-fit testing. Journal of Information and Computational Science, 2012, 13 (9): 3839- 3847.
30	GRECO M, GINI F, FARINA A. Radar detection and classification of jamming signals based on cone classes. IEEE Trans. on Signal Processing, 2008, 56 (5): 1984- 1993. doi: 10.1109/TSP.2007.909326
31	SKOLNIK M I. Introduction to radar system. 3rd ed. New York: McGraw-Hill, 2002.
32	YANG Y, GAO W, ZHANG X. Robust Kalman filtering with constraints: a case study for integrated navigation. Journal of Geodesy, 2010, 84 (6): 373- 381. doi: 10.1007/s00190-010-0374-6
33	SCHUHMACHER D, VO B T, VO B N. A consistent metric for performance evaluation of multi-object filters. IEEE Trans. on Signal Processing, 2008, 56 (8): 3447- 3457. doi: 10.1109/TSP.2008.920469
34	VO B T, VO B N, CANTONI A. The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Trans. on Signal Processing, 2009, 57 (2): 409- 423. doi: 10.1109/TSP.2008.2007924

Parameter	Value
False alarm probability	0.001
Mean SNR of true target/dB	10
JSR/dB	3
Number of bits of the DRFM	3
Sampling frequency of DRFM/GHz	1.024
Intermediate frequency of the DRFM/MHz	100
Number of samples	32

Method	a-CRR	Stable a-CRR
The proposed method	0.916 5	0.938 6
Method 1	0.785 6	0.821 0
Method 2	0.594 6	0.615 9
Method 3	0.925 5	0.925 6
Method 4	0.892 8	0.914 2

Method	Computing time
The proposed method	0.017 69
MDA-PHD	0.013 86
JPDA	3.540 2

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Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming

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