重尾噪声环境下的自适应TPMBM滤波器

doi:10.12305/j.issn.1001-506X.2025.09.04

Abstract

Abstract:

To address the target tracking problem of unknown heavy-tailed noise statistical characteristics, the trajectory Poisson multi-Bernoulli mixture filtering （TPMBM） algorithm based on Gamma student’s t-distribution inverse Wishar is proposed. The algorithm uses student’s t-distribution and inverse Wishart distribution to model the extended state of noise and extended target, and embeds multivariate multiple filter （MMF） into the TPMBM filter to estimate innovative characteristics, adaptively adjusts the noise degree of freedom and scale matrix. Multi window fusion technology is used to further improve the adaptive estimation ability of MMF. The simulation results demonstrate that, compared with existing algorithms, the proposed algorithm exhibits the best performance in tracking accuracy, centroid error, and intersection over union （IOU） shape fitting. The proposed algorithm reduces the centroid error by 15% and improves the IOU shape fitting by 10%. It also demonstrates higher estimation accuracy and robustness in the influence of noise in heavy-tailed noise environments.

Key words: heavy-tail noise, student’s t-distribution, extended target tracking, trajectory Poisson multi-Bernoulli mixture （TPMBM） filtering

CLC Number:

TN 953

Cuiyun LI, Zeyu ZHAO, Shuangwu ZHANG. Adaptive TPMBM filter in heavy-tailed noise environment[J]. Systems Engineering and Electronics, 2025, 47(9): 2808-2817.

Figures/Tables 18

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References 30

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目标	目标初始运动状态	存活时间/s
1	$ {{\boldsymbol{x}}_1} = {[ - 200\;{\mathrm{m}}, - 200\;{\mathrm{m}},5.2\;{\mathrm{m/s}},4.87\;{\mathrm{m/s}}]^{\mathrm{T}}} $	$ {t_1} \in [5,80] $
2	$ {{\boldsymbol{x}}_2} = {[ - 200\;{\mathrm{m}},200\;{\mathrm{m}},6.2\;{\mathrm{m/s}}, - 5.38\;{\mathrm{m/s}}]^{\mathrm{T}}} $	$ {t_2} \in [15,65] $
3	$ {{\boldsymbol{x}}_3} = {[200\;{\mathrm{m}}, - 200\;{\mathrm{m}}, - 6.82\;{\mathrm{m/s}},6.2\;{\mathrm{m/s}}]^{\mathrm{T}}} $	$ {t_3} \in [15,70] $
4	$ {{\boldsymbol{x}}_4} = {[200\;{\mathrm{m}},200\;{\mathrm{m}}, - 5.63\;{\mathrm{m/s}}, - 6.32\;{\mathrm{m/s}}]^{\mathrm{T}}} $	$ {t_4} \in [20,60] $
5	$ {{\boldsymbol{x}}_5} = {[ - 300\;{\mathrm{m}},175\;{\mathrm{m}},7\;{\mathrm{m/s}}, - 6.32\;{\mathrm{m/s}}]^{\mathrm{T}}} $	$ {t_5} \in [25,80] $

噪声类型	噪声特性
$ {w}_i$	$\begin{gathered}{\mathrm{N}}\left(0,\left\|\sin (k) \boldsymbol{R}_k\right\|\right) \\p_{{\mathrm{m o}}}=0.05\end{gathered}$
$ v_i$	$\begin{gathered}N\left(0,4 k \boldsymbol{R}_k\right) \\p_{{\mathrm{m o}}}=0.05\end{gathered}$

滤波器	平均误差/m
GGIW-TPMBM	10.906 9
GSTIW-TPMBM	6.966 9
MMF-GSTIW-TPMBM	5.028 4

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[2]	Bowen HENG, Cuiyun LI, Xiang LI. 3D extended target PMBM tracking algorithm based on moving least square [J]. Systems Engineering and Electronics, 2023, 45(11): 3411-3418.
[3]	LI Wenjuan, LV Jing, GU Hong, SU Weimin. Extended target tracking based on beta Gaussian probability hypothesis density [J]. Systems Engineering and Electronics, 2018, 40(9): 1897-1904.
[4]	LIU Yong, YAO Libo, XIU Jianjuan, XIONG Wei, ZHOU Zhimin. Ship target tracking with satellite electronic information based on improved MHT [J]. Systems Engineering and Electronics, 2017, 39(6): 1189-1196.

Adaptive TPMBM filter in heavy-tailed noise environment

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