Sequential Gaussian Approximation Filter for Target Tracking With Nonsynchronous Measurements

作     者：Yang, Xusheng Zhang, Wen-An Yu, Li Yang, Fuwen 

作者机构：Zhejiang Univ Technol Dept Automat Hangzhou 310023 Zhejiang Peoples R China Zhejiang Prov United Key Lab Embedded Syst Hangzhou 310023 Zhejiang Peoples R China Griffith Univ Griffith Sch Engn Gold Coast Qld 4222 Australia 

出 版 物：《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》 (IEEE航空航天与电子系统汇刊)

年 卷 期：2019年第55卷第1期

页      面：407-418页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：National Natural Science Foundation of China NSFC-Zhejiang Joint Fund for the Intergration of Industrialization and Informatization [U1709213] Zhejiang Provincial Natural Science Foundation of China [LZ15F030003, LR16F030005] 

主　　题：Noise measurement Wireless sensor networks Mobile robots Kalman filters Measurement uncertainty Target tracking Bayes methods 

摘      要：This paper presents an adaptive sequential fusion estimation method for the target tracking with nonsynchronous measurements in wireless sensor networks (WSNs). Based on Gaussian assumption and Bayesian inference, a sequential cubature Kalman filtering (SCKF) method, as well as its square root form (SR-SCKF), is presented by applying the cubature rule to approximate the function x Gaussian integrals. By taking into consideration the time-varying properties of the measurement noise and the linearization errors, some adaptive factors are introduced into the SCKF to compensate for the measurement uncertainties based on Chi-square tests. The convergence analysis of the SCKF is presented. It is proved that the adaptive SCKF (ASCKF) has a better convergence property than the SCKF. Both simulations and experiments of a target tracking example are presented to show the effectiveness and superiority of the proposed ASCKF method.