最優(yōu)算術(shù)平均融合及其在非同視域場景的應(yīng)用
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中圖分類號：TP911 文獻標志碼：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
分布式融合跟蹤通過整合多個傳感器節(jié)點的濾波輸出來提升跟蹤精度[，被廣泛應(yīng)用于防御、攻擊和工業(yè)監(jiān)控等領(lǐng)域，目前的研究聚焦于數(shù)目時變的不確定目標跟蹤。（剩余13484字）