Distributed optimal Kalman filtering for collaboration estimation in wireless sensor networks

In wireless sensor networks (WSNs), sensor nodes with limited resource usually need to exchange information with neighbor nodes to collaboratively finish some tasks. Based on minimum error covariance trace principle, a class of distributed optimal Kalman filters (DOKF) is proposed to cooperatively process information in WSNs, where each sensor node communicates only to its neighbors. To reduce computation complexity, the other class of DOKF with uniform form is also proposed for collaborative information processing. The performance analysis of the two classes of filters shows they have high estimation accuracy, low communication traffic, and reduced computation complexity. Thus, the proposed filters are much suitable to large-scale WSNs. We apply the proposed algorithms to estimate and track the position of a moving target in WSNs. Simulation illustrates that the proposed algorithms have superior performance.