Adaptive distributed estimation for mobile sensor networks based on cubature Kalman filtering

To deal with the nonlinearity in the target tracking and distributed estimation in mobile sensor networks, an algorithm of adaptive distributed estimation for mobile sensor networks based on cubature Kalman filtering is proposed by combining the advantages of the cubature Kalman filtering and consensus algorithm. The weight matrix in the consensus iteration changes dynamically according to the error between the node and its neighbors to improve the convergence rate of consensus iteration. Besides, the estimation error of the algorithm is proved to be bounded in mean square. A simulation example of mobile sensors in the sky tracking target on the ground is given to validate the effectiveness of the proposed adaptive distributed estimation.