Cost-effective diffusion Kalman filtering with implicit measurement exchanges

A resource effective extension to the class of distributed real-time diffusion Kalman filters is proposed. The proposed scheme removes the need to share measurement variables explicitly, by sharing only the state estimates and state error covariance matrices which implicitly contain the information about the measurements, observations matrices, and noise covariance matrices. The proposed distributed Kalman filter is quiet general, and its performance is comparable to that of existing diffusion based schemes, while having lower communication and computational requirements per-iteration compared to current distributed Kalman filtering algorithms.