PHD Particle Filter Method for Tracking Maneuvering Targets Based on Adaptive Interactive Multiple Models
Received date: 2014-05-03
Online published: 2025-05-26
危璋 , 冯新喜 , 毛少锋 . 自适应交互多模型的PHD粒子滤波多机动目标跟踪[J]. 弹箭与制导学报, 2015 , 35(2) : 166 -170 . DOI: 10.15892/j.cnki.djzdxb.2015.02.043
To solve the problem that multiple model probability hypothesis density (IMM-PHD) filter for maneuvering target tracking uses the prior model transition probability, a adaptive algorithm to Markova transition probability proposed. Firstly, the particles and the likelihood every model in the process of particles interaction, and then the output of every model to every target according to assortment in the process of state estimation, lastly, the model transtions probability by Bayes principle. The results show: compared IMM-PHD, AIMM-PHD has lower OSPA error; higher accuracy of target number estimation but its time only increases 8.1%, thus the effectiveness of the proposed algorithm.
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