Innovation Sharing Distributed Kalman Filter with Packet Loss

This study investigates the problem of distributed state estimation. A distributed Kalman filter algorithm is proposed, in which sensors exchange their innovations. A detailed analysis is conducted for the case of two sensor networks, demonstrating that the proposed algorithm outperforms the case where each sensor runs a conventional Kalman filter without communication. The upper bounds of error covariance matrices are also derived in the case of packet loss. Numerical examples verify the effectiveness of the proposed algorithm.