Distributed Fusion Filter for Networked Multi-sensor Systems with Unknown Measurement Interferences and Packet Dropouts

Abstract

Abstract. This paper is concerned with the design of distributed fusion filter for networked systems with unknown measurement interferences and packet dropouts. A Bernoulli distributed random variable is used to depict the phenomenon of packet dropouts. Without any prior information about the interference, a recursive Kalman-type state filter independent of the unknown interferences is designed for each sensor subsystem by applying the linear unbiased minimum variance estimation criterion. Based on the state filters of individual subsystems, the estimation error cross-covariance matrices between any two subsystems are derived. Then, the distributed fusion filter is designed by using the matrix-weighted fusion estimation algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.

Multi-Sensor Fusion Adaptive Estimation for Nonlinear Under-Observed System with Multiplicative Noise

“…Recently, a series of incremental algorithms have been proposed [5]- [7]. The extended incremental Kalman filtering (EIKF) and volumetric incremental Kalman filtering (CIKF) algorithms were presented in [8], [9], for nonlinear under-observed systems, respectively.Extended incremental Kalman filter (EIKF) is an incremental algorithm for the measurement equation based on the extended Kalman filter (EKF), which has the advantage of approximately eliminating the unknown measurement error and improving the estimation accuracy. However, the above papers only adopted additive noise, not multiplicative noise.…”

“…[7], Fu et al [8], Ma et al [9], It has higher accuracy and broader application prospects. The effectiveness and feasibility of the proposed algorithm are verified by simulating the multi-sensor fusion estimation using a fusion algorithm weighted by a scalar.…”