Distributed Finite-Horizon Fusion Kalman Filtering for Bandwidth and Energy Constrained Wireless Sensor Networks

This paper is concerned with the distributed finite-horizon fusion Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) in a bandwidth and energy constrained wireless sensor network. To satisfy the finite communication bandwidth, only partial components of each local vector estimate are allowed to be transmitted to the fusion center (FC) at a particular time, while each sensor intermittently sends information to the FC for reducing energy consumptions. At the FC end, a novel compensation strategy is proposed to compensate the untransmitted components of each local estimates, then a recursively distributed fusion Kalman filter (DFKF) is derived in the linear minimum variance sense. Notice that the designed DFKF update does not need to know the transmitting situation of each component at a particular time, which means that the proposed fusion estimation algorithm is easily implemented for the addressed NMFSs. Since the performance of the designed DFKF is dependent on the selecting probability of each component, some criteria for the choice of probabilities are derived such that the mean squared errors (MSEs) of the designed DFKF are bounded or convergent. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.