Distributed Consensus Kalman Filtering Over Time-Varying Graphs

This paper proposes an improved method for distributed consensus Kalman filtering (DCKF). We introduce a minor modification to the consensus kalman filter proposed in (Olfati-Saber (2009)). Namely an extra averaging term is introduced into the filter update equations. In this direction, we propose a decentralized consensus gain that can be computed by each agent in the sensor network, and depends only on local properties of the network, i.e., the number of neighbors of each sensor. Moreover we prove that this scheme is stable for networks with time varying communication regime. Our results are compared to other existing solutions in the literature with a numerical example.