Consensus Sigma-Point Information Filter for Large-Scale Sensor Networks
We address an estimation problem of nonlinear dynamic system through a large-scale sensor network. Even though much research has been done in data fusion, the extension to nonlinear dynamic system is recently focused. The main difficulty in data fusion of nonlinear dynamic system comes from that effective nonlinear filters do not allow the information form. In this paper, two algorithms are considered to implement distributed Kalman filtering for a large-scale sensor network. Data fusion problem for a largescale sensor network is tackled by using Kalman-Consensus filter (KCF) whose scalability is suitable for a large-scale sensor network with random topology. Based on KCF fusion algorithm, Sigma-Point Information filter (SPIF) is proposed as a micro-filter of KCF to handle the nonlinear dynamic system. Because of its information fusion structure, it is simple and intuitive to be combined with the consensus algorithm. Newly proposed algorithm called Consensus Sigma-Point Information Filter (CSPIF) shows us the improved accuracy compared with local estimates.
Kalman filtering nonlineaer dynamic systems distributed data fusion
Du Yong Kim Ju Hong Yoon Vladimir Shin
Dept. of Mechatronics Gwangju Institute of Science and Technology Gwangju, Korea
国际会议
电子商务、工程及科学领域的分布计算和应用国际会议(DCABES 2010)
香港
英文
268-272
2010-08-10（万方平台首次上网日期，不代表论文的发表时间）