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CLC number: TN973.3

On-line Access: 2019-08-05

Received: 2018-06-25

Revision Accepted: 2019-05-09

Crosschecked: 2019-07-12

Cited: 0

Clicked: 2748

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhi-yong Song

http://orcid.org/0000-0002-3833-0510

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.7 P.988-1001

http://doi.org/10.1631/FITEE.1800394


A novel algorithm to counter cross-eye jamming based on a multi-target model


Author(s):  Zhi-yong Song, Xing-lin Shen, Qiang Fu

Affiliation(s):  National Key Laboratory of Science and Technology on ATR, National University of Defense Technology, Changsha 410073, China

Corresponding email(s):   songzhiyong08@nudt.edu.cn

Key Words:  Particle identity labels, Probability hypothesis density, Cross-eye jamming, Anti-jamming, Random finite set, Monopulse radar


Zhi-yong Song, Xing-lin Shen, Qiang Fu. A novel algorithm to counter cross-eye jamming based on a multi-target model[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 988-1001.

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Abstract: 
cross-eye jamming is an electronic attack technique that induces an angular error in the monopulse radar by artificially creating a false target and deceiving the radar into detecting and tracking it. Presently, there is no effective anti-jamming method to counteract cross-eye jamming. In our study, through detailed analysis of the jamming mechanism, a multi-target model for a cross-eye jamming scenario is established within a random finite set framework. A novel anti-jamming method based on multi-target tracking using probability hypothesis density filters is subsequently developed by combining the characteristic differences between target and jamming with the releasing process of jamming. The characteristic differences between target and jamming and the releasing process of jamming are used to optimize particle partitioning. particle identity labels that represent the properties of target and jamming are introduced into the detection and tracking processes. The release of cross-eye jamming is detected by estimating the number of targets in the beam, and the distinction between true targets and false jamming is realized through correlation and transmission between labels and estimated states. Thus, accurate tracking of the true targets is achieved under severe jamming conditions. Simulation results showed that the proposed method achieves a minimum delay in detection of cross-eye jamming and an accurate estimation of the target state.

一种基于多目标模型的抗交叉眼干扰新方法

摘要:交叉眼干扰是一种电子攻击手段,它通过人为构造虚假目标并欺骗雷达对其进行检测和跟踪,从而导致单脉冲雷达产生角度误差。目前还没有能够有效对抗交叉眼干扰的方法。本文通过对交叉眼干扰详细的机理分析,在随机有限集框架下建立描述典型交叉眼干扰场景的多目标模型。将目标与干扰的特征差异以及交叉眼干扰的释放过程结合,提出一种基于概率假设密度多目标滤波器的抗干扰新方法。目标与干扰的特征差异以及干扰释放的过程信息可用于优化粒子的划分。将表征目标和干扰特性的粒子身份标签引入目标检测和跟踪流程,通过波束内目标数目的实时估计检测干扰的释放,通过粒子标签与估计状态之间的关联和传递实现真实目标与虚假干扰的身份辨别,从而在强干扰条件下实现对真实目标的准确跟踪。仿真结果表明,所提抗干扰方法具有很小的干扰检测延迟以及较高的目标状态估计精度。

关键词:粒子身份标签;概率假设密度;交叉眼干扰;抗干扰;随机有限集;单脉冲雷达

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bai K, Wang YH, 2013. Millimeter-wave radar and IR fusion anti-jamming method based on the analysis of target activities. J Huazhong Univ Sci Technol (Nat Sci Ed), 41(S1):163-166 (in Chinese).

[2]Beard M, Reuter S, Granström K, et al., 2016. Multiple extended target tracking with labeled random finite sets. IEEE Trans Signal Process, 64(7):1638-1653.

[3]Bryant DS, Vo BT, Vo BN, et al., 2018. A generalized labeled multi-Bernoulli filter with object spawning. IEEE Trans Signal Process, 66(23):6177-6189.

[4]Clark DE, Bell J, 2007. Multi-target state estimation and track continuity for the particle PHD filter. IEEE Trans Aerosp Electron Syst, 43(4):1441-1453.

[5]du Plessis WP, 2012. Platform skin return and retrodirective cross-eye jamming. IEEE Trans Aerosp Electron Syst, 48(1):490-501.

[6]du Plessis WP, 2016. Path-length effects in multiloop retrodirective cross-eye jamming. IEEE Antenn Wirel Propag Lett, 15:626-629.

[7]du Plessis WP, Odendaal JW, Joubert J, 2009. Extended analysis of retrodirective cross-eye jamming. IEEE Trans Antenn Propag, 57(9):2803-2806.

[8]du Plessis WP, Odendaal JW, Joubert J, 2011. Experimental simulation of retrodirective cross-eye jamming. IEEE Trans Aerosp Electron Syst, 47(1):734-740.

[9]Fantacci C, Papi F, 2016. Scalable multisensor multitarget tracking using the marginalized δ-GLMB density. IEEE Signal Process Lett, 23(6):863-867.

[10]Hong S, Wang L, Shi ZG, et al., 2011. Simplified particle PHD filter for multiple-target tracking: algorithm and architecture. Prog Electromagn Res, 120:481-498.

[11]KRET, 2014. Missiles are not a problem: the SAP 518 jamming station protects fighter jets from guided missiles. http://www.kret.com/en/news/3544/

[12]Li J, Shen W, 2015. Analysis of anti-jamming capability of multistatic radar system under different interference rules. Acta Armam, 36(S2):178-185 (in Chinese).

[13]Li TC, Sun SD, Corchado JM, et al., 2014. A particle dyeing approach for track continuity for the SMC-PHD filter. Proc 17th IEEE Int Conf on Information Fusion, p.1-8.

[14]Li TC, Corchado JM, García J, et al., 2016. MEAP: approximate optimal estimate extraction for the SMC-PHD filter. Proc 19th IEEE Int Conf on Information Fusion, p.2309-2316.

[15]Li TC, Corchado JM, Sun SD, et al., 2017. Multi-EAP: extended EAP for multi-estimate extraction for SMC-PHD filter. Chin J Aeronaut, 30(1):368-379.

[16]Li XR, Jilkov VP, 2003. Survey of maneuvering target tracking. Part I. Dynamic models. IEEE Trans Aerosp Electron Syst, 39(4):1333-1364.

[17]Li YZ, Hu WQ, Chen X, et al., 2013. Research on polarization discrimination algorithm for coherent dual-source angle deception interference. Acta Armam, 34(9):1078-1083 (in Chinese).

[18]Lin L, Bar-Shalom Y, Kirubarajan T, 2006. Track labeling and PHD filter for multitarget tracking. IEEE Trans Aerosp Electron Syst, 42(3):778-795.

[19]Mahler RPS, 2003. Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans Aerosp Electron Syst, 39(4):1152-1178.

[20]Mahler RPS, 2007. Statistical Multisource Multitarget Information Fusion. Artech House, Norwood, MA, USA, p.194-211.

[21]Mahler RPS, 2014. Advances in Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood, MA, USA, p.307-329.

[22]Papi F, Vo BN, Vo BT, et al., 2015. Generalized labeled multi-Bernoulli approximation of multi-object densities. IEEE Trans Signal Process, 63(20):5487-5497.

[23]Reuter S, Vo BT, Vo BN, et al., 2014. The labeled multi-Bernoulli filter. IEEE Trans Signal Process, 62(12):3246-3260.

[24]Schuhmacher D, Vo BT, Vo BN, 2008. A consistent metric for performance evaluation of multi-object filters. IEEE Trans Signal Process, 56(8):3447-3457.

[25]Shi ZG, Zheng Y, Bian X, et al., 2013. Threshold-based resampling for high-speed particle PHD filter. Prog Electromagn Res, 136:369-383.

[26]Ulmke M, Erdinc O, Willett P, 2007. Gaussian mixture cardinalized PHD filter for ground moving target tracking. Proc 10th Int Conf on Information Fusion, p.1-8.

[27]Vo BN, Sing S, Doucet A, 2005. Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Trans Aerosp Electron Syst, 41(4):1224-1245.

[28]Vo BT, 2008. Random Finite Sets in Multi-object Filtering. PhD Thesis, University of Western Australia, Australia, p.127-144.

[29]Vo BT, Vo BN, 2013. Labeled random finite sets and multi-object conjugate priors. IEEE Trans Signal Process, 61(13):3460-3475.

[30]Vo BT, Vo BN, Cantoni A, 2007. Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Trans Signal Process, 55(7):3553-3567.

[31]Vo BT, Vo BN, Cantoni A, 2008. Bayesian filtering with random finite set observations. IEEE Trans Signal Process, 56(4):1313-1326.

[32]Vo BT, Vo BN, Cantoni A, 2009. The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Trans Signal Process, 57(2):409-423.

[33]Wu W, Wang GH, Liu Y, et al., 2011. Airborne radar/IRST/ ESM synergistic tracking and management. Syst Eng Electron, 33(7):1517-1522 (in Chinese).

[34]Xiao HT, Li YX, Fu Q, 2015. Identification and tracking of towed decoy and aircraft using multiple-model improved labeled P-PHD filter. Dig Signal Process, 46:49-58.

[35]Xue D, Dong WF, Gao JM, et al., 2011. Performance analysis of time-domain discrimination technique of ARUAV on countering three active sources. Radar Sci Technol, 9(6):496-501.

[36]Zhao SS, Zhang LR, Zhou Y, et al., 2014. Measurement fusion method against false-target jamming for radar network. J Univ Electron Sci Technol China, 43(2):207-211.

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