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

This paper mainly studies the fusion estimation problem of the networked multi-sensor systems with multiplicative noises. Firstly, the state space model is transformed into a new system with fictitious noises to obtain the local Kalman filter. Secondly, applying the Sequential Inverse Covariance Intersection (SICI) fusion algorithm, the SICI fusion estimator is presented, which avoids the computational burden of the cross-covariance among local estimators. Compared with the Sequential Covariance Intersection (SCI) fusion algorithm, the SICI fusion algorithm has lower conservativeness, and is proved that its estimation accuracy is higher than those of the local filters and SCI fusion estimator. A simulation example shows the effectiveness and consistency of the presented fusion estimators.