Diffusion distributed Kalman filter over sensor networks without exchanging raw measurements

In this paper, we propose a new diffusion strategy based distributed state estimation algorithm over sensor networks. In the proposed algorithm, every sensor only communicates with their neighboring sensors, and only intermediate estimation information is exchanged to avoid sharing raw measurements, which may be unavailable or inconvenient to be transmitted under some circumstances. Local estimations are obtained through a new method and the convex combination weights are obtained through covariance intersection (CI) technology. To further release the communication burden and energy consuming , one simplified algorithm is also given, where the local and final estimations are fused at a selected rate. We analyze the mean and convergence performances of proposed algorithms under some assumptions. Numerical simulations show that the first algorithm has better estimation accuracy when comparing with several existing diffusion based methods, and the latter simplified algorithm has good estimation accuracy but greatly reduced communication burden and energy consuming.