交互式多模型概率数据关联抗速度拖引干扰算法

doi:10.12305/j.issn.1001-506X.2025.05.13

摘要/Abstract

摘要：

脉冲多普勒雷达导引头面对释放速度拖引干扰的强机动性目标时, 存在跟踪、制导精度下降的问题。为此, 结合空空/地空导弹应用场景, 提出一种基于交互式多模型概率数据关联的抗速度拖引干扰算法。该算法结合概率数据关联和多模型交互的优势, 采用概率数据关联滤波器处理角度和速度通道的量测信息, 同时引入多模型交互框架动态适应目标在不同机动状态下的运动模式。仿真实验表明, 提出的抗干扰算法可增强对复杂机动行为的捕捉能力和目标状态估计的鲁棒性, 有效降低速度拖引干扰带来的影响, 提升导弹制导精度。

关键词: 强机动目标跟踪, 速度拖引干扰, 交互式多模型, 概率数据关联

Abstract:

Pulse Doppler (PD) radar seekers face the issue of degraded tracking and guidance accuracy when confronted with highly maneuverable targets employing velocity gate pull-off (VGPO) jamming. To address this, an anti-VGPO algorithm based on interacting multiple model (IMM) probability data association (PDA) is proposed, specifically tailored for air-to-air/air-to-ground missile applications. This algorithm combines the strengths of PDA and IMM, utilizing PDA filters to process measurement information from angle and velocity channels, while incorporating an IMM framework to dynamically adapt to the target motion patterns under various maneuvering states. Simulation experiments demonstrate that the proposed anti-jamming algorithm enhances the capability to capture complex maneuvering behaviors and improves the robustness of target state estimation. Simultaneously, it effectively mitigates the impact of velocity pull-off jamming and enhances the missile guidance accuracy.

Key words: highly maneuverable target tracking, velocity gate pull-off (VGPO) jamming, interacting multiple model (IMM), probability data association (PDA)

中图分类号:

TJ765.3

闫咏琪, 王琪, 易凡. 交互式多模型概率数据关联抗速度拖引干扰算法[J]. 系统工程与电子技术, 2025, 47(5): 1507-1515.

Yongqi YAN, Qi WANG, Fan YI. Algorithm for countering velocity gate pull-off jamming based on interacting multiple model probability data association[J]. Systems Engineering and Electronics, 2025, 47(5): 1507-1515.

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参考文献 30

1	HE C , WEI S P , LI Y C , et al. Feature-aided RGPO jamming discrimination within wideband radar maneuvering target tracking[J]. IEEE Trans. on Aerospace and Electronic Systems, 2023, 59 (6): 7938- 7950. doi: 10.1109/TAES.2023.3299440
2	WANG H X , WANG H W , WANG X , et al. Analysis of the Doppler frequency feature of aero deception active-passive-jamming[J]. Mathematical Structures in Computer Science, 2014, 24 (5): e240507. doi: 10.1017/S0960129513000686
3	WU C Z , CHEN B X . A recognition algorithm of VGPO jamming based on discrete chirp-Fourier transform[J]. EURASIP Journal on Advances in Signal Processing, 2020, 2020, 37. doi: 10.1186/s13634-020-00694-3
4	TUCK A F , THRUSH B A , BROWN J M . Maneuvering target tracking with anti-RGPO jamming ability based on radar plot processing[J]. Telecommunication Engineering, 2023, 63 (2): 212- 219.
5	WU L P, FU X J, LIU S L, et al. The anti-jamming method based on front-back-edge tracking of VGPO jamming[C]//Proc. of the CIE International Conference on Radar, 2016.
6	PURUSHOTTAMA L , BETHI P , PATHIPATI S . Sequential fusion based approach for estimating range gate pull-off parameter in a networked radar system: an ECCM algorithm[J]. IEEE Access, 2022, 10, 70902- 70918. doi: 10.1109/ACCESS.2022.3185240
7	刘代, 赵永波, 郭敏, 等. 一种杂波环境下机动目标跟踪算法[J]. 电子科技大学学报, 2020, 49 (2): 213- 218.
	LIU D , ZHAO Y B , GUO M , et al. A method for maneuvering target tracking in clutter[J]. Journal of University of Electronic Science and Technology of China, 2020, 49 (2): 213- 218.
8	贾舒宜, 王子玲, 唐田田. 距离-速度同步欺骗干扰下的机动目标跟踪算法[J]. 系统工程与电子技术, 2017, 39 (9): 1942- 1949. doi: 10.3969/j.issn.1001-506X.2017.09.05
	JIA S Y , WANG Z L , TANG T T . Research on tracking of ma neuvering targets in simultaneous range and velocity jamming[J]. Systems Engineering and Electronics, 2017, 39 (9): 1942- 1949. doi: 10.3969/j.issn.1001-506X.2017.09.05
9	AZIZ M A . A new nearest-neighbor association approach based on fuzzy clustering[J]. Aerospace Science and Technology, 2013, 26 (1): 87- 97. doi: 10.1016/j.ast.2012.02.017
10	恽鹏, 吴盘龙, 李星秀, 等. 变分贝叶斯概率数据关联算法[J]. 自动化学报, 2022, 48 (10): 2486- 2495.
	YUN P , WU P L , LI X X , et al. Variational Bayesian probabilistic data association algorithm[J]. Acta Automatica Sinica, 2022, 48 (10): 2486- 2495.
11	BAR-SHALOM Y , DAUM F , HUANG J . The probabilistic data association filter[J]. IEEE Control Systems Magazine, 2012, 29 (6): 82- 100.
12	包守亮, 程水英, 许登荣. RGPO干扰下基于改进的IMM-PDAF的机动目标跟踪技术[J]. 现代雷达, 2018, 40 (2): 60- 67.
	BAO S L , CHENG S Y , XU D R . A maneuvering target tracking technique based on modified IMM-PDAF under RGPO jamming[J]. Modern Radar, 2018, 40 (2): 60- 67.
13	CHENG T, HE Z S, LI Y X. An effective target tracking algorithm with anti-RGPO ability[C]//Proc. of the 10th IEEE International New Circuits and Systems Conference, 2012: 117-120.
14	耿利祥, 陈钱, 钱惟贤. 改进的联合概率数据关联算法(JPDA)对红外目标与诱饵的辨别[J]. 红外与激光工程, 2013, 42 (2): 305- 310.
	GENG L X , CHEN Q , QIAN W X . Infrared aircraft-flare discrimination using improved JPDA algorithm[J]. Infrared and Laser Engineering, 2013, 42 (2): 305- 310.
15	王琪, 廖志忠, 燕飞. 基于概率数据关联的雷达导引头抗速度拖引干扰算法[J]. 系统工程与电子技术, 2022, 44 (2): 448- 454. doi: 10.12305/j.issn.1001-506X.2022.02.12
	WANG Q , LIAO Z Z , YAN F . Algorithm for countering velocity gate pull-off jamming of radar seeker based on probability data association[J]. Systems Engineering and Electronics, 2022, 44 (2): 448- 454. doi: 10.12305/j.issn.1001-506X.2022.02.12
16	XIE A, YIN F, AI B, et al. Learning while tracking: a practical system based on variational Gaussian process state-space model and smartphone sensory data[C]//Proc. of the 23rd International Conference on Information Fusion, 2020.
17	MA Y J , ZHAO S Y , HUANG B . Multiple-model state estimation based on variational Bayesian inference[J]. IEEE Trans. on Automatic Control, 2019, 64 (4): 1679- 1685.
18	FAN X H, EDWIN V, TERENCE J, et al. Free-form variational inference for Gaussian process state-space models[EB/OL]. [2024-01-26]. https://arxiv.org/abs/2302.09921.
19	曾浩, 母王强, 杨顺平. 高机动目标跟踪ATPM-IMM算法[J]. 通信学报, 2022, 43 (7): 93- 101.
	ZENG H , MU W Q , YANG S P . High maneuvering target tracking ATPM-IMM algorithm[J]. Journal on Communications, 2022, 43 (7): 93- 101.
20	LIM J , KIM H S , PARK H M . Interactive multiple model algorithm based on minimax particle filtering[J]. IEEE Signal Processing Letters, 2020, 27, 36- 40.
21	AFTAB W , MIHAYLOVA L . A learning Gaussian process approach for maneuvering target tracking and smoothing[J]. IEEE Trans. on Aerospace and Electronic Systems, 2021, 57 (1): 278- 292.
22	梁彦刚, 郝道亮, 唐国金. 捷联导引头视线转率估计的交互式多模型样条滤波算法[J]. 国防科技大学学报, 2014, 36 (5): 70- 74.
	LIANG Y G , HAO D L , TANG G J . Spline filtering algorithm based on interacting multiple model for line-of-sight rate estimation in strap-down seeker system[J]. Journal of National University of Defense Technology, 2014, 36 (5): 70- 74.
23	吕梅柏, 赵小锋, 刘广哲. 空中大机动目标跟踪算法研究[J]. 现代防御技术, 2018, 46 (2): 45- 50.
	LV M B , ZHAO X F , LIU G Z . Aerial high maneuvering target tracking algorithm[J]. Modern Defence Technology, 2018, 46 (2): 45- 50.
24	韩崇昭, 朱洪艳, 段战胜. 多源信息融合[M]. 北京: 清华大学出版社, 2010.
	HAN C Z , ZHU H Y , DUAN Z S . Multi-source information fusion[M]. Beijing: Press of Tsinghua University, 2010.
25	MEI W, BAR-SHALOM Y. Unbiased Kalman filter using converted measurements: revisit[C]//Proc. of the Signal and Data Processing of Small Targets, 2009.
26	ZHANG R, ZUO L, WANG P F, et al. IMMPDA algorithm for missile tracking in clutter[C]//Proc. of the International Conference on Control, Automation and Information Sciences, 2021.
27	LUCIANA B C , MARCOS F S , WALMIR M C , et al. An adaptive approach for estimation of transition probability matrix in the interacting multiple model filter[J]. Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology, 2021, 41 (1): 155- 166.
28	LIU Z G, LU J G, DONG Z. Research on penetration technology of intelligent cluster missile system[C]//Proc. of the International Conference on Robots Intelligent System, 2019: 245-249.
29	GONG J W , WANG X F , YANG R J . Cooperative tracking technology of single target multi-sensor based on cooperative index[J]. Mobile Networks and Applications, 2019, 25 (4): 1594- 1603.
30	CHEN X , LI Z W , HU X D , et al. Research on radar/infrared fusion algorithm based on IMM/MS-MDPDAF[J]. Journal of Physics: Conference Series, 2019, 1213 (3): 032001.

符号	定义	数值
V_T/(m/s)	目标速度	260
V_M/(m/s)	导弹速度	1 000
D₀/m	导弹目标相对距离	18 000
θ_M/(°)	导弹方位角	15
θ_T/(°)	目标方位角	180

符号	定义	数值
T_{J, 1}/s	第1重线性干扰的周期	6.0
V_{J, 1}/(m/s)	第1重线性干扰的范围	100
T_{J, 2}/s	第2重线性干扰的周期	9.0
V_{J, 2}/(m/s)	第2重线性干扰的范围	200
T_{J, 3}/s	第3重线性干扰的周期	12.0
V_{J, 3}/(m/s)	第3重线性干扰的范围	-200

算法	脱靶量
IMMPDA算法/m	0.6
IMMSEKF算法/m	2
PDA算法/m	51

符号	定义	数值
T_{J, 1}/s	第1重线性干扰的周期	6.0
V_{J, 1}/(m/s)	第1重线性干扰的范围	120
T_{J, 2}/s	第2重线性干扰的周期	9.0
V_{J, 2}/(m/s)	第2重线性干扰的范围	-150
T_{J, 3}/s	第3重抛物线干扰的周期	8.0
V_{J, 3}/(m/s)	第3重抛物线干扰的范围	100
T_{J, 4}/s	第4重抛物线干扰的周期	10.0
V_{J, 4}/(m/s)	第4重抛物线干扰的范围	-200

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