A Cooperative Kalman Consensus Filter via Indirectly State Vector Fusion

This paper studies the distributed state estimation problem for a multi-sensor network where each sensing node has different process model which means that the state estimation cannot be fused directly with other nodes’ estimation states. When the discrete time-varying process model and affine transformation between two estimation spaces of multi-sensor network are a prior information, to get a consistent estimation set of target, the transformed process model of each sensing nodes is derived, and a consensus based distributed Kalman filtering algorithm via indirect state vector fusion (SVF) strategy is designed. If the communication topology of network is connected, the estimation errors of sensors are globally asymptotically stable without the process and measurement noises. In the existence of the process and measurement noises, it is proven that the estimation errors are exponentially bounded in mean square and bounded with probability 1. And the boundary of inverse covariance error matrix and Kalman gain are derived in the meantime.