Sequential Inverse Covariance Intersection Fusion Kalman Filter for Networked Systems with Multiplicative Noises and Correlated Noise

In this paper, the problem of fusion estimation for networked multi-sensor systems with multiplicative noise and correlated noise is studied. First, the state space model is transformed into a new system with fictitious noises and local Kalman filter are obtained. Then the sequential inverse covariance intersection (SICI) fusion algorithm is applied and the SICI fusion estimator is proposed to avoid the computation burden of local estimators. Compared with the Sequential Covariance Intersection (SCI) fusion algorithm, SICI fusion algorithm is less conservative, and its estimation accuracy is higher than that of local filter and SCI fusion estimator. The validity and consistency of the proposed fusion estimator are verified by a simulation example.