A novel square-root cubature information weighted consensus filter algorithm for distributed camera networks

This paper deals with the problem of tracking target in a distributed camera network using the cubature Kalman filter.The square-root cubature Information filter (SCIF) is an extension of the cubature Kalman filter.It is an efficient and robust non-linear filter for multi-sensor data fusion.However,when this algorithm is applied to large-scale networks such as camera networks,the center node may be imposed on severe computational loads if using centralized multi-sensor system.In order to solve this problem,a distributed algorithm based on square-root cubature information filter is presented for large-scale networks.In camera networks,because cameras are arranged in a larger region,the target may appear in the blind zone due to the limited field of view (FOV).This may produce invalid measurements from some cameras.To overcome this problem,this paper proposes a novel square-root cubature information weighted consensus filter (SCIWCF) which reduces the effect of these invalid measurements in consensus algorithm via proper weighting on the information vector and information matrix.The simulation results show that the proposed algorithm can efficiently track the target in camera networks,and is obviously better in terms of its accuracy and stability than the traditional Information filter.