Self-tuning Distributed Measurement Fusion Kalman Filter

摘要：

Based on weighted least squares method(WLS), a equivalent fusion measurement equation is obtained for the multisensor linear discrete stochastic time-invariant system with unknown noise statistics and the measurement matrices having the same right factor. Using the modern time series analysis method, based on on-line identification of the moving average(MA) innovation model parameters, unknown noise variances can on-line be estimated, and a self-tuning weighted measurement fusion Kalman filter is presented. Under the assumptions that the parameter estimation of the MA innovation model is consistent and the measurement data is bounded. It is proved that self-tuning Kalman filter converges to globally optimal fusion Kalman filter with known noise statistics, so that it has asymptotic global optimality. A simulation example for a tracking system with 4-sensor shows its effectiveness.

关键词： Multisensor Weighted measurement fusion Self-tuning Kalman filters Identification noise variance estimation

作者: Yun Li Jintao Yu Ming Zhao Ke Han

作者单位: School of Computer and Information Engineering Harbin University of Commerce Harbin,Heilongjiang Province,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-5

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）