Full Text:
<10773>
Summary: 
<1559>
CLC number: TP391; TN953
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-09-11
Cited: 0
Clicked: 5262
Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems
| ||||||||||||||
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, 2021, 22(1): 79-87.
@article{title="Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems",
author="Weihua Wu, Yichao Cai, Hongbin Jin, Mao Zheng, Xun Feng, Zewen Guan",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="1",
pages="79-87",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000105"
}
%0 Journal Article
%T Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems
%A Weihua Wu
%A Yichao Cai
%A Hongbin Jin
%A Mao Zheng
%A Xun Feng
%A Zewen Guan
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 1
%P 79-87
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000105
TY - JOUR
T1 - Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems
A1 - Weihua Wu
A1 - Yichao Cai
A1 - Hongbin Jin
A1 - Mao Zheng
A1 - Xun Feng
A1 - Zewen Guan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 1
SP - 79
EP - 87
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000105
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.
[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.
ORCID: 
Open peer comments: Debate/Discuss/Question/Opinion
<1>