基于逆滤波处理的雷达干扰效果在线评估方法

doi:10.12305/j.issn.1001-506X.2023.09.08

Abstract

Abstract:

Evaluation of the jamming effect under non-cooperative conditions is a key and difficult problem in realizing the closed loop of cognitive electronic countermeasures. Thus, a method that can estimate the internal tracking error of the radar system through inverse filtering processing of the external states of the radar system is proposed. This method can realize the online evaluation of the jamming effectiveness for the current jamming action and optimizes the corresponding jamming parameters. This paper firstly models the inverse filter processing problem to estimate the radar tracking error, and then derives the inverse filter processing algorithm for typical radar filter algorithms. Specifically, the method of online jamming effectiveness evaluation and jamming parameter optimization for range gate pull off (RGPO) jamming is designed. Simulation results with RGPO self-defense jammer against the seeker radar prove the feasibility and effectiveness of the proposed method.

Key words: radar countermeasure, online jamming effectiveness evaluation, inverse signal processing, inverse probabilistic data association (PDA) filter, range gate pull off (RGPO)

CLC Number:

TN974

Rui LI, Mengtao ZHU, Yunjie LI. Online evaluation method of radar jamming effect based on inverse filtering processing[J]. Systems Engineering and Electronics, 2023, 45(9): 2706-2717.

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References 33

22	KRISHNAMURTHY V , RANGASWAMY M . How to calibrate your adversary's capabilities? Inverse filtering for counter-autonomous systems[J]. IEEE Trans.on Signal Processing, 2019, 67 (24): 6511- 6525. doi: 10.1109/TSP.2019.2956676
23	KUPTAMETEE C , AUNSRI N . A review of resampling techniques in particle filtering framework[J]. Measurement, 2022, 193, 110836. doi: 10.1016/j.measurement.2022.110836
24	XIE Z , ZHU J H , FAN C Y , et al. Robust range ambiguous deceptive target suppression based on covariance matrix reconstruction[J]. Remote Sensing, 2021, 13 (12): 2346. doi: 10.3390/rs13122346
25	马定坤, 匡银, 杨新权. 侦干探通一体化现状与关键技术研究[J]. 中国电子科学研究院学报, 2016, 11 (5): 457- 462. doi: 10.3969/j.issn.1673-5692.2016.05.001
	MA D K , KUANG Y , YANG X Q . Key issues and status research of integrated reconnaissance, inteference, detection and communications[J]. Journal of China Academy of Electronic and Information Technology, 2016, 11 (5): 457- 462. doi: 10.3969/j.issn.1673-5692.2016.05.001
26	HE S , SHIN H S , TSOURDOS A . Distributed joint probabilistic data association filter with hybrid fusion strategy[J]. IEEE Trans.on Instrumentation and Measurement, 2019, 69 (1): 286- 300.
27	王宏强, 秦玉亮, 李宏, 等. 修正的PDA算法[J]. 信号处理, 2009, 25 (3): 430- 434. doi: 10.3969/j.issn.1003-0530.2009.03.017
	WANG H Q , QIN Y L , LI H , et al. Modified PDA algorithm and performance prediction[J]. Journal of Signal Processing, 2009, 25 (3): 430- 434. doi: 10.3969/j.issn.1003-0530.2009.03.017
28	KERSHAW D J , EVANS R J . Optimal waveform selection for tracking systems[J]. IEEE Trans.on Information Theory, 1994, 40 (5): 1536- 1550. doi: 10.1109/18.333866
29	何友, 修建娟, 张晶炜, 等. 雷达数据处理及应用[M]. 2版北京: 电子工业出版社, 2009.
	HE Y , XIU J J , ZHANG J W , et al. Radar data processing with applications[M]. 2nd ed Beijing: Publishing House of Electronics Industry, 2009.
1	LI X, LIU D S. Modeling method of combat mission based on OODA loop[C]//Proc. of the MATEC Web of Conferences, 2022.
2	CHU J Y , XIAO N , LIANG J . Jamming effect evaluation method based on radar behavior recognition[J]. Journal of Physics: Conference Series, 2020, 1629 (1): 012001. doi: 10.1088/1742-6596/1629/1/012001
3	JIN Y C , JIANG J P , ZHU J . Neural network based fuzzy identification and its application to modeling and control of complex systems[J]. IEEE Trans.on Systems, Man and Cybernetics, 1995, 26 (5): 990- 997.
4	KWON H M , SCHAEFER L T . Neural network applications for jamming state information generator[J]. IEEE Trans.on Neural Networks, 1994, 5 (5): 833- 837. doi: 10.1109/72.317735
5	李潮, 周金泉. 基于干扰方的干扰效果评估研究[J]. 电子信息对抗技术, 2008, 23 (2): 46- 49. doi: 10.3969/j.issn.1674-2230.2008.02.012
	LI C , ZHOU J Q . Jamming effectiveness evaluation from the Jaming side[J]. Electronic Information Warfare Technology, 2008, 23 (2): 46- 49. doi: 10.3969/j.issn.1674-2230.2008.02.012
6	OU J, ZHAO F, AI X F, et al. Quantitative evaluation for self-screening jamming effectiveness based on the changing characteristics of intercepted radar signals[C]//Proc. of the IEEE CIE International Conference on Radar, 2016.
7	翁鑫锦. 基于机器学习的雷达干扰效能评估[D]. 成都: 电子科技大学, 2019.
	WENG X J. Effectiveness evaluation of radar jamming based on machine learning[D]. Chengdu: University of Electronic Science and Technology of China, 2019.
8	KALMAN R E . When is a linear control system optimal[J]. Journal of Basic Engineering, 1964, 86 (1): 51- 60. doi: 10.1115/1.3653115
9	ZHAO C Y , WANG Q , LIU X F , et al. Reinforcement learning based a non-zero-sum game for secure transmission against smart jamming[J]. Digital Signal Processing, 2021, 112 (12): 103002.
10	CHOI J, KIM K E. Nonparametric Bayesian inverse reinforcement learning for multiple reward functions[C]//Proc. of the 25th International Conference on Neural Information Processing Systems, 2015: 305-313.
11	KLEIN E, GEIST M, PIOT B, et al. Inverse reinforcement learning through structured classification[C]//Proc. of the Advances in Neural Information Processing Systems, 2012.
12	ARORA S , DOSHI P . A survey of inverse reinforcement learning: challenges, methods and progress[J]. Artificial Intelligence, 2021, 297, 103500. doi: 10.1016/j.artint.2021.103500
13	PERRUSQUIA A . A complementary learning approach for expertise transference of human-optimized controllers[J]. Neural Networks, 2022, 145, 33- 41. doi: 10.1016/j.neunet.2021.10.009
14	KRISHNAMURTHY V , ANGLEY D , EVANS R , et al. Identifying cognitive radars inverse reinforcement learning using revealed preferences[J]. IEEE Trans.on Signal Processing, 2020, 68, 4529- 4542. doi: 10.1109/TSP.2020.3013516
15	KRISHNAMURTHY V , PATTANAYAK K , GOGINENI S , et al. Adversarial radar inference: inverse tracking, identifying cognition, and designing smart interference[J]. IEEE Trans.on Aerospace and Electronic Systems, 2021, 57 (4): 2067- 2081. doi: 10.1109/TAES.2021.3090901
16	MATTILA R, ROJAS C R, KRISHNAMURTHY V, et al. Inverse filtering for linear Gaussian state-space models[C]//Proc. of the IEEE Conference on Decision and Control, 2018: 5556-5561.
17	GUSTAFSSON F . Adaptive filtering and change detection[M]. New York: Wiley, 2000.
18	CHEN J , PATTON R J . Robust model-based fault diagnosis for dynamic systems[M]. Boston: Springer Science & Business Media, 2012.
19	ANDERSON B , MOORE J B , ESLAMI M . Optimal filtering[J]. IEEE Trans.on Systems, Man and Cybernetics, 2007, 12 (2): 235- 236.
20	MATTILA R, ROJAS C R, KRISHNAMURTHY V, et al. Inverse filtering for hidden Markov models[C]//Proc. of the 31st International Conference on Neural Information Processing Systems, 2017.
21	MATTILA R , ROJAS C R , KRISHNAMURTHY V , et al. Inverse filtering for hidden Markov models with applications to counter-adversarial autonomous systems[J]. IEEE Trans.on Signal Processing, 2020, 68, 4987- 5002. doi: 10.1109/TSP.2020.3019177
30	贾瑞. 雷达距离门拖引干扰策略智能生成方法研究[D]. 成都: 电子科技大学, 2021.
	JIA R. Research on intelligent generation method of radar range gate pull of jamming strategy[D]. Chengdu: University of Electronic Science and Technology of China, 2021.
31	陈玉文, 童幼堂. 卡尔曼滤波法抗距离拖引干扰研究[J]. 飞航导弹, 2000, (3): 50- 51.
	CHEN Y W , TONG Y T . Researoh on Kalman filter method for antirange dragging interference[J]. Aerospace Technology, 2000, (3): 50- 51.
32	WEISS M , SHIMA T . Optimal linear-quadratic missile gui-dance laws with penalty on command variability[J]. Journal of Guidance, Control, and Dynamics, 2015, 38 (2): 226- 237. doi: 10.2514/1.G000738
33	HULL D G , SPEYER J L , BURRIS D B . Linear-quadratic guidance law for dual control of homing missiles[J]. Journal of Guidance, Control and Dynamics, 2016, 13 (1): 137- 144.

突变点检测结果	干扰效果评估		策略调整
突变点检测结果	结果	时刻	过程控制	参数调整
相继检测到起拖和成功突变点	成功	成功突变点时刻	当前策略继续拖引	无
相继检测到起拖和失败突变点	Ⅰ型失败	失败突变点时刻	即刻重启下次拖引	减小拖引速度
拖引期只检测到起拖突变点	Ⅱ型失败	拖引期结束时刻	即刻重启下次拖引	增大拖引速度
拖引期过半未检测到起拖突变点	Ⅲ型失败	拖引期中间时刻	即刻重启下次拖引	减小拖引速度

拖引干扰参数名称	参数设定
停拖期时长/s	1
干扰信号与回波信号幅度比	1.4
拖引期时长/s	8
关闭期时长/s	1
拖引周期/s	10
拖引速度/(m·s^-1)	50~700

拖引速度/(m·s^-1)	评估正确性	评估时延/s	跟踪误差估计 RMSE/m
50	正确	0	4.89
100	正确	0.02	7.25
200	正确	0.02	7.80
300	正确	0.02	7.48
400	正确	0.02	7.07
500	正确	0.02	6.05
700	正确	4	1.28

r_us	评估正确性	评估时延/s	跟踪误差估计 RMSE/m
0	正确	0.02	5.82
1	正确	0.02	9.78
10	正确	0.08	10.96
20	正确	0.85	11.11
30	正确	1.15	11.15
40	正确	1.83	11.17
50	正确	2.02	11.22
100	正确	2.62	11.28

导航比	评估正确性	评估时延/s	跟踪误差估计 RMSE/m
0.1	正确	4.07	11.31
0.5	正确	2.22	11.24
1	正确	0.40	11.00
2	正确	0.04	10.39
3	正确	0.04	9.84
4	正确	0.02	9.38
5	正确	0.02	9.03

Online evaluation method of radar jamming effect based on inverse filtering processing

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