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CLC number: TP391; TN953

On-line Access: 2021-01-11

Received: 2020-03-11

Revision Accepted: 2020-06-30

Crosschecked: 2020-09-11

Cited: 0

Clicked: 4282

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Weihua Wu

https://orcid.org/0000-0002-8737-3525

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Frontiers of Information Technology & Electronic Engineering 

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Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems


Author(s):  Weihua Wu, Yichao Cai, Hongbin Jin, Mao Zheng, Xun Feng, Zewen Guan

Affiliation(s):  Department of Early Warning Intelligence, Air Force Early Warning Academy, Wuhan 430019, China

Corresponding email(s):  weihuawu1987@163.com

Key Words:  Multi-maneuvering-target tracking, Multi-model, Generalized labeled multi-Bernoulli filter, Multi-target hybrid systems


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Weihua Wu, Yichao Cai, Hongbin Jin, Mao Zheng, Xun Feng, Zewen Guan. Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000105

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author="Weihua Wu, Yichao Cai, Hongbin Jin, Mao Zheng, Xun Feng, Zewen Guan",
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doi="https://doi.org/10.1631/FITEE.2000105"
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%A Hongbin Jin
%A Mao Zheng
%A Xun Feng
%A Zewen Guan
%J Frontiers of Information Technology & Electronic Engineering
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T1 - Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems
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A1 - Zewen Guan
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Abstract: 
In this study, we extend traditional (single-target) hybrid systems to multi-target hybrid systems with a focus on the multi-maneuvering-target tracking system. This system consists of a continuous state, a discrete and switchable state, and a discrete, time-constant, and unique state. By defining a new generalized labeled multi-Bernoulli density, we prove that it is closed under the Chapman-Kolmogorov prediction and Bayes update for multi-target hybrid systems. In other words, we provide the exact derivation of a solution to this system, i.e., the multi-model generalized labeled multi-Bernoulli filter, which has been developed without strict proof.

多模型广义标签多伯努利滤波器的推导:多目标混合系统解决方案


吴卫华,蔡益朝,金宏斌,郑茂,冯讯,关泽文
空军预警学院预警情报系,中国武汉市,430019

摘要:本文将传统(单目标)混合系统扩展到多目标混合系统,重点研究多机动目标跟踪系统。该系统由连续状态,离散可切换状态以及离散、时不变且唯一性状态组成。通过定义一个新的广义标签多伯努利密度,我们证明对于多目标混合系统,它在查普曼-柯尔莫哥洛夫(Chapman-Kolmogorov)预测和贝叶斯更新下是闭合的。换言之,我们严格推导了多目标混合系统的解决方案,即多模型广义标签多伯努利滤波器--该滤波器虽已被开发,但此前并未得到严格证明。

关键词组:多机动目标跟踪;多模型;广义标签多伯努利滤波器;多目标混合系统

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

Reference

[1]Bar-Shalom Y, Challa S, Blom HAP, 2005. IMM estimator versus optimal estimator for hybrid systems. IEEE Trans Aerosp Electron Syst, 41(3):986-991.

[2]Chang CB, Athans M, 1978. State estimation for discrete systems with switching parameters. IEEE Trans Aerosp Electron Syst, 14(3):418-425.

[3]Dunne D, Kirubarajan T, 2013. Multiple model multi-Bernoulli filters for manoeuvering targets. IEEE Trans Aerosp Electron Syst, 49(4):2679-2692.

[4]Fortmann TE, Bar-Shalom Y, Scheffe M, 1980. Multi-target tracking using joint probabilistic data association. Proc 19th IEEE Conf on Decision and Control Including the Symp on Adaptive Processes, p.807-812.

[5]Georgescu R, Willett P, 2012. The multiple model CPHD tracker. IEEE Trans Signal Process, 60(4):1741-1751.

[6]Hwang I, Balakrishnan H, Tomlin C, 2006. State estimation for hybrid systems: applications to aircraft tracking. IEE Proc Contr Theory Appl, 153(5):556-566.

[7]Jiang M, Yi W, Hoseinnezhad R, et al., 2016. Adaptive Vo-Vo filter for maneuvering targets with time-varying dynamics. Proc 19th Int Conf on Information Fusion, p.666-672.

[8]Li XR, Jilkov VP, 2005. Survey of maneuvering target tracking. Part V: multiple-model methods. IEEE Trans Aerosp Electron Syst, 41(4):1255-1321.

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

[10]Mahler RPS, 2012. On multitarget jump-Markov filters. Proc 15th Int Conf on Information Fusion, p.149-156.

[11]Mahler RPS, 2014. Advances in Statistical Multisource-Multitarget Information Fusion. Artech House, Boston, USA.

[12]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.

[13]Pasha SA, Vo BN, Tuan HD, et al., 2009. A Gaussian mixture PHD filter for jump Markov system models. IEEE Trans Aerosp Electron Syst, 45(3):919-936.

[14]Punchihewa Y, 2017. Efficient generalized labeled multi-Bernoulli filter for jump Markov system. Proc Int Conf on Control, Automation and Information Sciences, p.221-226.

[15]Punchihewa Y, Vo BT, Vo BN, 2016. A generalized labeled multi-Bernoulli filter for maneuvering targets. Proc 19th Int Conf on Information Fusion, p.980-986.

[16]Punithakumar K, Kirubarajan T, Sinha A, 2008. Multiple-model probability hypothesis density filter for tracking maneuvering targets. IEEE Trans Aerosp Electron Syst, 44(1):87-98.

[17]Reid D, 1979. An algorithm for tracking multiple targets. IEEE Trans Autom Contr, 24(6):843-854.

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

[19]Reuter S, Scheel A, Dietmayer K, 2015. The multiple model labeled multi-Bernoulli filter. Proc 18th Int Conf on Information Fusion, p.1574-1580.

[20]Seah CE, Hwang I, 2009. State estimation for stochastic linear hybrid systems with continuous-state-dependent transitions: an IMM approach. IEEE Trans Aerosp Electron Syst, 45(1):376-392.

[21]Sithiravel R, McDonald M, Balaji B, et al., 2016. Multiple model spline probability hypothesis density filter. IEEE Trans Aerosp Electron Syst, 52(3):1210-1226.

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

[23]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.

[24]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.

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

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

[27]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.

[28]Wood TM, 2011. Interacting methods for manoeuvre handling in the GM-PHD filter. IEEE Trans Aerosp Electron Syst, 47(4):3021-3025.

[29]Wu W, Sun H, Cai Y, et al., 2020. Tracking multiple maneuvering targets hidden in the DBZ based on the MM-GLMB filter. IEEE Trans Signal Process, 68:2912-2924.

[30]Yang JL, Ji HB, Ge HW, 2013. Multi-model particle cardinality-balanced multi-target multi-Bernoulli algorithm for multiple manoeuvring target tracking. IET Radar Sonar Navig, 7(2):101-112.

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

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