Distributed Fusion Filter for Nonlinear Multi-Sensor Systems With Correlated Noises

作     者：Hao, Gang Sun, Shuli 

作者机构：Heilongjiang Univ Sch Elect Engn Harbin 150080 Peoples R China Key Lab Informat Fus Estimat & Detect Harbin 150080 Heilongjiang Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2020年第8卷

页      面：39548-39560页

核心收录：

基　　金：National Natural Science Foundation (NNSF) of China [61503127, 61573132] University Basic Research foundation of Heilongjiang Province [KJCX201901] Outstanding Youth Foundation of Heilongjiang University Key Laboratory of Information Fusion Estimation and Detection, Heilongjiang Province 

主　　题：Estimation Nonlinear systems Noise measurement Mathematical model Kalman filters Covariance matrices Gaussian noise Multi-sensor nonlinear system distributed fusion filter cross-covariance matrix linear minimum variance estimation 

摘      要：This paper is concerned with distributed fusion (DF) estimation problem for nonlinear multi-sensor systems with correlated noises. Based on a recursive linear minimum variance estimation (RLMVE) framework, a novel filter is developed. It is proved that the RLMVE-based filter and the existing de-correlated filter have the functional equivalence. Then, for multi-sensor cases, cross-covariance matrices between any two local filters are derived. Based on the RLMVE-based filter and cross-covariance matrices, a DF filter weighted by matrices is proposed in the sense of linear minimum variance. Finally, based on the existing de-correlated filter, the algorithm of cross-covariance for de-correlated systems and the DF algorithm weighted by matrices, a de-correlated DF filtering algorithm is proposed. An example verifies the effectiveness of the proposed RLMVE-based DF filter.