Distributed Fusion Kalman Filtering with Communication Constraints

This paper is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) under the assumptions that (i) distributed sensors have computation capabilities, (ii) the communication between the sensors and the fusion center (FC) is subject to finite communication bandwidth. The communication bandwidth constraint considered is that only partial components of the local vector estimation signals are allowed to be transmitted to the FC at a particular time, and multiple binary variables are introduced to model this component transmitting process. A novel compensation strategy is proposed to restructure the local estimation signal at the FC end, and a recursively distributed fusion kalman filter (DFKF) is designed in the linear minimum variance sense from the restructured local unbiased-estimators. It is shown that the mean-square error (MSE) of the designed DFKF is dependent on the introduced binary variables, and a simply suboptimal judgement criterion is proposed to determine a group of binary variables such that MSE of the designed DFKF is minimal at each time step.