Fusion of Gaussian Mixture Models for Maneuvering Target Tracking in the Presence of Unknown Cross-correlation

作     者：ZHU Hongyan GUO Kai CHEN Shuo 

作者机构：School of Electronic and Information EngineeringXi'an Jiaotong University China Electronics Technology Group Corporation No.28 Research Institute 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2016年第25卷第2期

页      面：270-276页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0701[理学-数学] 

基　　金：supported by the National Natural Science Foundation of China(No.61203220) the National Basic Research Program of China(973 Program)(No.2013CB329405) 

主　　题：Gaussian mixture model(GMM) Estimation fusion Interacting multiple model(IMM) Covariance intersection(CI) Cross-correlation 

摘      要：The paper addresses the problem of estimation fusion for maneuvering target tracking in the presence of unknown cross-correlation. To improve the fusion accuracy, two major points are concerned. Firstly, the Interacting multiple model(IMM) estimator is performed for each sensor, and the local estimate is represented by a Gaussian mixture model instead of a Gaussian density to keep more details of the local tracker. Next, a close-formed solution of fusing two Gaussian mixtures in the Covariance intersection(CI) framework is derived to cope with the unknown cross-correlation of local estimation errors. Experimental results demonstrate that the proposed approach provides some improvements in the fusion accuracy over the competitive methods.