A novel robust filtering strategy for systems with Non-Gaussian noises

作     者：Zhou, Hongping Xu, Lin Chen, Wenliang Guo, Kai Shen, Fei Guo, Zhongyi 

作者机构：Hefei Univ Technol Sch Comp & Informat Tunxi Rd 193 Hefei Anhui Peoples R China 

出 版 物：《AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS》 (电子学与通信技术文献)

年 卷 期：2018年第97卷

页      面：154-164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] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：National Natural Science Foundation of China Natural Science Foundation of Anhui Province, China [1808085MF188, 1808085QA21] 

主　　题：Gaussian sum filter Non-Gaussian noise Smooth variable structure filter Target tracking 

摘      要：For estimating the states of moving targets in the nonlinear system with non-Gaussian noise, the combination of Gaussian Sum Filter (GSF) and other nonlinear filters has been chosen as the filtering algorithm conventionally. The Smooth Variable Structure Filter (SVSF) is a new predictor-corrector method used for state and parameter estimation, which has good stability and robustness. In this paper we propose a new strategy called the modified GS-EKF-SVSF, which inherits good robustness of Gaussian Sum and Smooth Variable Structure Filter (GS-SVSF) and high accuracy of Gaussian Sum and Extended Kalman Filter (GS-EKF). A nonlinear system with non-Gaussian noise for target tracking is used to test the proposed new strategy. The simulation results demonstrate that our proposed strategy has higher accuracy and better robustness when there are modelling uncertainties existing in the system. (C) 2018 Elsevier GmbH. All rights reserved.