A novel variable structure multi-model approach based on error-ambiguity decomposition  

作　　者：Han SHEN-TU Yingjiao RONG Dongliang PENG Mengfan XUE Yunfei GUO 

机构地区：[1]Institution of Information and Control,Hangzhou Dianzi University,Hangzhou 310018,China [2]Science and Technology on Near-Surface Detection Laboratory,Wuxi 214035,China

出　　处：《Chinese Journal of Aeronautics》2020年第6期1731-1746,共16页中国航空学报（英文版）

基　　金：funded by the National Natural Science Foundation of China(Nos.61703128,61871166,61701148,61703131);the Science and Technology on Near-Surface Detection Laboratory Foundation,China(No.6142414180208);the Zhejiang Provincial Natural Science Foundation of China(No.LZ20F010002)。

摘　　要：Model Set Adaptation(MSA)plays a key role in the Variable Structure Multi-Model tracking approach(VSMM).In this paper,the Error-Ambiguity Decomposition(EAD)principle is adopted to derive the EAD-MSA criterion that is optimal in the sense of minimizing the square error between the estimate and the truth.Consequently,the EAD Variable Structure first-order General Pseudo Bayesian(EAD-VSGPB1)algorithm and the EAD Variable Structure Interacting Multiple Model(EAD-VSIMM)algorithm are constructed.The proposed algorithms are tested in two groups of maneuvering target tracking scenarios under different modes and observation error conditions.The simulation results demonstrate the effectiveness of the EAD-VSMM approach and show that,compared to some existing multi-model algorithms,the proposed EAD-VSMM algorithms achieve more robust and accurate tracking results.

关 键 词：Error-ambiguity decomposi­tion Maneuvering target tracking Model sequence set adapta­tion Multi-model estimation Variable structure 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]