最优算术平均融合及其在非同视域场景的应用

doi:10.12305/j.issn.1001-506X.2025.06.02

Abstract

Abstract:

A decorrelation arithmetic average (AA) fusion algorithm of Gaussian mixture probability hypothesis density (GM-PHD) filters is proposed to achieve optimal tracking of a time-varying number of uncertain targets within different field of view (FOV). Given that the multi-target AA fusion is decomposed into multiple groups of single-target component merging by association operation, optimal decorrelation estimation fusion is firstly derived by reshaping the Bayesian fusion and then is applied as the merging method of single-target components. Since the derived decorrelation estimation fusion requires prior estimates, a hierarchical structure involving a master filter dedicated to automatically providing prior estimates is designed. To address the underestimated target cardinality arising from different FOV, the fusion node compensates for weight of single-target components according to FOV. Simulation results demonstrate the proposed algorithm's optimality in various scenarios, which improves the multi-target tracking accuracy.

Key words: probability hypothesis density (PHD) filter, decorrelation, Bayesian fusion, hierarchical structure, master filter

CLC Number:

TP911

Yu XUE, Xi'an FENG. Optimal arithmetic average fusion and its application in different fields of view[J]. Systems Engineering and Electronics, 2025, 47(6): 1739-1745.

Figures/Tables 10

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编号i	初始状态x₁ⁱ/(m, m, m/s, m/s)	存活时间/s
目标-1	700, 950, 2.5, -8	2~100
目标-2	300, 700, 12, -2	13~80
目标-3	300, 700, 25, 5	25~60
目标-4	1 200, 900, 3, -16	30~55
目标-5	1 200, 400, 4, 30	35~50
目标-6	800, 300, 1, 10	10~70

算法	均值	最大值	标准差
GM-PHD	6.684 9	9.334 6	1.496 9
标准AA	6.210 6	9.306 9	1.177 7
补偿权重GA	5.189 6	9.046 0	1.235 3
补偿权重-AA	4.961 5	8.516 1	1.291 6
去相关AA	4.273 2	8.326 5	1.175 6

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