Multiple Extended Target Tracking in the Presence of Heavy-Tailed Noise Using Multi-Bernoulli Filtering Method

被引：0

作者：

Chen H. ^{[1
]}

Zhang X.-X. ^{[1
]}

机构：

[1] School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2023年 / 49卷 / 07期

基金：

中国国家自然科学基金;

关键词：

heavy-tailed noise; multi-Bernoulli; Multiple extended target tracking (METT); random hypersurface;

D O I：

10.16383/j.aas.c201061

中图分类号：

学科分类号：

摘要：

Aiming at the problem of the irregular star-convex multiple extended target tracking (METT) with heavy-tailed noise, a multiple extended target tracking in the presence of heavy-tailed noise using multi-Bernoulli filtering method is proposed in this article. First, the student＇s t distribution is used to model the heavy-tailed process noise and measurement noise. The irregular star-convex multiple extended target filtering problem is formulated based on the finite set statistics (FISST) theory and the random hypersurface model (RHM). Then, the multi-Bernoulli density is approximated by the student＇s t mixture (STM) and a student＇s t mixture multiple extended target multi-Bernoulli filter (STM-MET-CBMeMBer) is proposed correspondingly. Furthermore, this article proposes a nonlinear robust student＇s t mixture star-convex multiple extended target multi-Bernoulli filter based on the robust student＇s t based cubature filtering method. Finally, simulation experiments on star-convex multiple extended target tracking and multiple group target tracking with the heavy-tail noise verify effectiveness of the proposed method. © 2023 Science Press. All rights reserved.

引用

页码：1573 / 1586

页数：13

共 38 条

[1]

Granstrom K, Fatemi M, Svensson L., Poisson multi-Bernoulli mixture conjugate prior for multiple extended target filtering, IEEE Transactions on Aerospace and Electronic Systems, 56, 1, (2020)

[2]

Yang S S, Teich F, Baum M., Network flow labeling for extended target tracking PHD filters, IEEE Transactions on Industrial Informatics, 15, 7, pp. 4164-4171, (2019)

[3]

Liu B, Tharmarasa R, Jassemi R, Brown D, Kirubarajan T., Extended target tracking with multipath detections, terrain-constrained motion model and clutter, IEEE Transactions on Intelligent Transportation Systems, 22, 11, pp. 7056-7072, (2021)

[4]

Granstrom K, Fatemi M, Svensson L., Gamma Gaussian inverse-Wishart Poisson multi-Bernoulli filter for extended target tracking, Proceedings of the 19th International Conference on Information Fusion (FUSION), pp. 893-900, (2016)

[5]

Vo B N, Vo B T., A multi-scan labeled random finite set model for multi-object state estimation, IEEE Transactions on Signal Processing, 67, 19, pp. 4948-4963, (2019)

[6]

Mahler R P S., Advances in Statistical Multisource-Multitarget Information Fusion, (2014)

[7]

Garcia-Fernandez A F, Svensson L., Trajectory PHD and CPHD filters, IEEE Transactions on Signal Processing, 67, 22, pp. 5702-5714, (2019)

[8]

Granstrom K, Lundquist C, Orguner O., Extended target tracking using a Gaussian-mixture PHD filter, IEEE Transactions on Aerospace and Electronic Systems, 48, 4, pp. 3268-3286, (2012)

[9]

Vo B T, Vo B N, Cantoni A., The cardinality balanced multi-target multi-Bernoulli filter and its implementations, IEEE Transactions on Signal Processing, 57, 2, (2009)

[10]

Vo B N, Vo B T, Hoang H G., An efficient implementation of the generalized labeled multi-Bernoulli filter, IEEE Transactions on Signal Processing, 65, 8, (2017)

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