适用于点群共存场景的TPMBM跟踪算法

doi:10.12305/j.issn.1001-506X.2024.07.04

Abstract

Abstract:

In order to solve the problem of low tracking accuracy of the traditional group target tracking algorithms in point-group coexistence scenarios, a trajectory Poisson multi-Bernoulli mixture (TPMBM) filte-ring algorithm is proposed, which can track both point target and group target simultaneously. The algorithm expands the state space of the target, introduces probability information about the target class based on the standard point target and the group target models, and achieves the judgment of the target class and the estimation of the target motion state through the prediction and update process of the TPMBM filter. Simulation results show that, compared with the existing algorithms, the proposed algorithm has significantly lower miss detection error and better tracking performance when point target and group target coexist.

Key words: target tracking, point-group coexistence, trajectory Poisson multi-Bernoulli mixture (TPMBM) filtering

CLC Number:

TN953

Shuangwu ZHANG, Cuiyun LI, Jingzhe ZHAO, Bowen HENG. TPMBM tracking algorithm suitable for point-group coexistence scenarios[J]. Systems Engineering and Electronics, 2024, 46(7): 2201-2210.

Figures/Tables 18

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References 31

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目标	目标类别	初始状态	存活时间/s
1	群目标	x₁=[－200, －200, 5, 5]^T	t₁∈[5, 70]
2	群目标	x₂=[－200, 200, 6, －4]^T	t₂∈[15, 90]
3	群目标	x₃=[200, 150, －4, －5]^T	t₃∈[20, 85]

目标	目标类别	初始状态	存活时间/s
1	群目标	x₁=[－200, －200, 5, 5]^T	t₁∈[5, 70]
2	群目标	x₂=[－200, 200, 6, －4]^T	t₂∈[15, 90]
3	群目标	x₃=[200, 150, －4, －5]^T	t₃∈[20, 85]
4	点目标	x₄=[－200, －100, 5, 5]^T	t₄∈[5, 70]
5	点目标	x₅=[－100, －300, 5, 5]^T	t₅∈[20, 80]

目标	目标类别	初始状态	存活时间/s
1	群→点	x₁=[－200, 200, 6, －4]^T	t_{1, 1}∈[15, 45] t_{1, 2}∈[46, 90]
2	群目标	x₂=[200, 150, －4, －5]^T	t₂∈[20, 85]
3	点目标	x₃=[－200, －100, 5, 5]^T	t₃∈[5, 70]
4	点↓群	x₄=[－100, －300, 5, 5]^T	t_{4, 1}∈[20, 49] t_{4, 2}∈[50, 80]

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TPMBM tracking algorithm suitable for point-group coexistence scenarios

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