Full Text:  <253>

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CLC number: TN953

On-line Access: 2022-12-14

Received: 2022-06-30

Revision Accepted: 2022-12-17

Crosschecked: 2022-10-10

Cited: 0

Clicked: 148

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Qiang GUO

https://orcid.org/0000-0002-8366-7163

Long TENG

https://orcid.org/0000-0003-3519-7790

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Frontiers of Information Technology & Electronic Engineering 

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Generalized labeled multi-Bernoulli filter with signal features of unknown emitters


Author(s):  Qiang GUO, Long TENG, Xinliang WU, Wenming SONG, Dayu HUANG

Affiliation(s):  College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; more

Corresponding email(s):  guoqiang@hrbeu.edu.cn, tenglong@hrbeu.edu.cn, wuxinliang51@163.com, hdayady@163.com

Key Words:  Multi-target tracking; Generalized labeled multi-Bernoulli; Signal features of emitter; Fuzzy C-means; Dynamic clustering


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Qiang GUO, Long TENG, Xinliang WU, Wenming SONG, Dayu HUANG. Generalized labeled multi-Bernoulli filter with signal features of unknown emitters[J]. Frontiers of Information Technology & Electronic Engineering , 2022, 23(10): 1871-1880.

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journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="12",
pages="1871-1880",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200286"
}

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T1 - Generalized labeled multi-Bernoulli filter with signal features of unknown emitters
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Abstract: 
A novel algorithm that combines the generalized labeled multi-Bernoulli (GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features (EFs) are often unknown and time-varying. Aiming at the unknown feature problem, we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown, an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements, to approximate the EF likelihood function. On this basis, the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations. Simulation results show that the proposed method can improve the tracking performance of multiple targets, especially in heavy clutter environments.

未知辐射源信号特征辅助的广义标签多伯努利滤波器

国强1,滕龙1,2,吴新良2,宋文明2,黄大羽2
1哈尔滨工程大学信息与通信工程学院,中国哈尔滨市,150001
2中国航空无线电电子研究所,中国上海市,200233
摘要:提出一种未知辐射源信号特征辅助的广义标签多伯努利滤波器。复杂电磁环境下,辐射源特征通常未知且随时间变化。针对辐射源特征未知的问题,提出一种基于数据场动态聚类的辐射源特征求解方法。针对辐射源特征时变以及对应的概率分布未知的问题,提出一种改进的模糊C-均值算法来计算目标和杂波量测的相关系数,以近似辐射源特征的似然函数。在此基础上,将辐射源特征集成到广义标签多伯努利滤波器中,从而获得新的递归方程。仿真结果表明,提出的方法可以提高对多目标的跟踪性能,尤其在强杂波环境中。

关键词组:多目标跟踪;广义标签多伯努利;辐射源信号特征;模糊C-均值;动态聚类

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

Reference

[1]Bar-Shalom Y, Kirubarajan T, Gokberk C, 2005. Tracking with classification-aided multiframe data association. IEEE Trans Aerosp Electron Syst, 41(3):868-878.

[2]Battistelli G, Chisci L, Fantacci C, et al., 2013. Consensus CPHD filter for distributed multitarget tracking. IEEE J Sel Top Signal Process, 7(3):508-520.

[3]Cao CH, Zhao YB, 2022. Range estimation based on symmetry polynomial aided Chinese remainder theorem for multiple targets in a pulse Doppler radar. Front Inform Technol Electron Eng, 23(2):304-316.

[4]Chen HM, Li XR, Bar-Shalom Y, 2004. On joint track initiation and parameter estimation under measurement origin uncertainty. IEEE Trans Aerosp Electron Syst, 40(2):675-694.

[5]Chen HM, Kirubarajan T, Bar-Shalom Y, 2008. Tracking of spawning targets with multiple finite resolution sensors. IEEE Trans Aerosp Electron Syst, 44(1):2-14.

[6]Clark D, Ristic B, Vo BN, et al., 2010. Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR. IEEE Trans Signal Process, 58(1):26-37.

[7]Da K, Li TC, Zhu YF, et al., 2020. Gaussian mixture particle jump-Markov-CPHD fusion for multitarget tracking using sensors with limited views. IEEE Trans Signal Inform Process Netw, 6:605-616.

[8]Da K, Li TC, Zhu YF, et al., 2021. Recent advances in multisensor multitarget tracking using random finite set. Front Inform Technol Electron Eng, 22(1):5-24.

[9]Guo Q, Nan PL, Wan J, 2016. Signal classification method based on data mining for multi-mode radar. J Syst Eng Electron, 27(5):1010-1017.

[10]Guo YF, Fan KS, Peng DL, et al., 2015. A modified variable rate particle filter for maneuvering target tracking. Front Inform Technol Electron Eng, 16(11):985-994.

[11]Guo YF, Tharmarasa R, Rajan S, et al., 2016. Passive tracking in heavy clutter with sensor location uncertainty. IEEE Trans Aerosp Electron Syst, 52(4):1536-1554.

[12]Guo YF, Li Y, Ren X, et al., 2020a. Multiple maneuvering extended target tracking based on Gaussian process. Acta Autom Sin, 46(11):2392-2403 (in Chinese).

[13]Guo YF, Li Y, Xue AK, et al., 2020b. Simultaneous tracking of a maneuvering ship and its wake using Gaussian processes. Signal Process, 172:107547.

[14]Herrmann M, Hermann C, Buchholz M, 2021. Distributed implementation of the centralized generalized labeled multi-Bernoulli filter. IEEE Trans Signal Process, 69:5159-5174.

[15]Jin B, Li C, Guo J, et al., 2019. Multi-target tracking in clutter aided by Doppler information. J Univ Electron Sci Technol China, 48(4):511-517 (in Chinese).

[16]Li TC, Hlawatsch F, 2021. A distributed particle-PHD filter using arithmetic-average fusion of Gaussian mixture parameters. Inform Fus, 73:111-124.

[17]Li TC, Sun SD, Bolić M, et al., 2016. Algorithm design for parallel implementation of the SMC-PHD filter. Signal Process, 119:115-127.

[18]Li TC, Su JY, Liu W, et al., 2017. Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front Inform Technol Electron Eng, 18(12):1913-1939.

[19]Li TC, Prieto J, Fan HQ, et al., 2018. A robust multi-sensor PHD filter based on multi-sensor measurement clustering. IEEE Commun Lett, 22(10):2064-2067.

[20]Li TC, Liu Z, Pan Q, 2019. Distributed Bernoulli filtering for target detection and tracking based on arithmetic average fusion. IEEE Signal Process Lett, 26(12):1812-1816.

[21]Liu C, Sun JP, Lei P, 2018. δ-generalized labeled multi-Bernoulli filter using amplitude information of neighboring cells. Sensors, 18(4):1153.

[22]Mahler R, 2007. PHD filters of higher order in target number. IEEE Trans Aerosp Electron Syst, 43(4):1523-1543.

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

[24]Mahler RPS, 2007. Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood, USA.

[25]Peng H, Huang G, Tian W, et al., 2018. Labeled multi-Bernoulli filter based on amplitude information. Syst Eng Electron, 40(12):2636-2641.

[26]Ristic B, Vo BT, Vo BN, et al., 2013. A tutorial on Bernoulli filters: theory, implementation and applications. IEEE Trans Signal Process, 61(13):3406-3430.

[27]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.

[28]Sun X, Li RW, Zhou LS, 2020. Multidimensional information fusion in active sonar via the generalized labeled multi-Bernoulli filter. IEEE Access, 8:211335-211347.

[29]Vo BN, Ma WK, 2006. The Gaussian mixture probability hypothesis density filter. IEEE Trans Signal Process, 54(11):4091-4104.

[30]Vo BN, Vo BT, Phung D, 2014. Labeled random finite sets and the Bayes multi-target tracking filter. IEEE Trans Signal Process, 62(24):6554-6567.

[31]Vo BN, Vo BT, Hoang HG, 2017. An efficient implementation of the generalized labeled multi-Bernoulli filter. IEEE Trans Signal Process, 65(8):1975-1987.

[32]Wang LP, Zhan RH, Huang Z, et al., 2021. Joint tracking and classification of extended targets with complex shapes. Front Inform Technol Electron Eng, 22(6):839-861.

[33]Wu WH, Cai YC, Jin HB, et al., 2021. Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems. Front Inform Technol Electron Eng, 22(1):79-87.

[34]Yi W, Chai L, 2021. Heterogeneous multi-sensor fusion with random finite set multi-object densities. IEEE Trans Signal Process, 69:3399-3414.

[35]Yi W, Jiang M, Hoseinnezhad R, 2017. The multiple model Vo-Vo filter. IEEE Trans Aerosp Electron Syst, 53(2):1045-1054.

[36]Yi W, Li GC, Battistelli G, 2020. Distributed multi-sensor fusion of PHD filters with different sensor fields of view. IEEE Trans Signal Process, 68:5204-5218.

[37]Zhou YQ, Zhu SL, 2015. GM-PHD filter with signal features of emitter. Asian J Contr, 17(5):1978-1983.

[38]Zhu Y, Liang S, Wu XJ, et al., 2021. A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain. Front Inform Technol Electron Eng, 22(8):1114-1126.

[39]Zhu YQ, 2015. Research on Tracking Techniques of Multiple Radar Emitter Targets Based on PHD Filter. PhD Thesis, National University of Defense Technology, Changsha, China (in Chinese).

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