Multi-Sensor Consensus Estimation of State, Sensor Biases and Unknown Input.
This paper addresses the problem of the joint estimation of system state and generalized sensor bias (GSB) under a common unknown input (UI) in the case of bias evolution in a heterogeneous sensor network. First, the equivalent UI-free GSB dynamic model is derived and the local optimal estimates of system state and sensor bias are obtained in each sensor node; Second, based on the state and bias estimates obtained by each node from its neighbors, the UI is estimated via the least-squares method, and then the state estimates are fused via consensus processing; Finally, the multi-sensor bias estimates are further refined based on the consensus estimate of the UI. A numerical example of distributed multi-sensor target tracking is presented to illustrate the proposed filter.
Key Laboratory of Information Fusion Technology, Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710129, China. zhoujie@mail.nwpu.edu.cn.
Jie
J
Zhou
Key Laboratory of Information Fusion Technology, Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710129, China. liangyan@nwpu.edu.cn.
Yan
Y
Liang
Key Laboratory of Information Fusion Technology, Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710129, China. yangfeng@nwpu.edu.cn.
Feng
F
Yang
Key Laboratory of Information Fusion Technology, Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710129, China. xulinf@gmail.com.
Linfeng
L
Xu
Key Laboratory of Information Fusion Technology, Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710129, China. quanpan@nwpu.edu.cn.
Quan
Q
Pan
bias estimation; network consensus; sensor registration; state estimation