最优算术平均融合及其在非同视域场景的应用

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中图分类号：TP911 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.06.02

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 diferent fieldof 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 fusionand then is applied as the merging method of singletarget components.Since the derived decorrelation estimation fusion requires prior estimates，a hierarchical structure involvinga master filter dedicated to automaticaly providing prior estimates is designed.To address theunderestimated 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.

Keywords：probability hypothesis density（PHD） filter；decorelation；Bayesian fusion；hierarchica structure；master filter

0 引言

分布式融合跟踪通过整合多个传感器节点的滤波输出来提升跟踪精度[，被广泛应用于防御、攻击和工业监控等领域，目前的研究聚焦于数目时变的不确定目标跟踪。（剩余13484字）