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.
wireless sensor networks collaboration estimation distributed Kalman filter
LIU Yonggui XU Bugong
College of Automation Science and Engineering, South China University of Technology Guangzhou 510640 College of Automation Science and Engineering, South China University of TechnologyGuangzhou 510640,
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
6540-6545
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）