Self-tuning multisensor measurement fusion Kalman filter

被引：0

作者：

Hao Gang ^{[1
]}

Jia Wenjing ^{[1
]}

Deng Zili ^{[1
]}

机构：

[1] Heilongjiang Univ, Dept Automat, Harbin 150080, Peoples R China

来源：

2006 CHINESE CONTROL CONFERENCE, VOLS 1-5 | 2006年

关键词：

multisensor; weighted measurement fusion; self-tuning Kalman filter; identification; noise variance estimation; convergence; asymptotic global optimality; modern time series analysis method;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For the multisensor system with unknown noise statistics, and with the measurement matrices having the same factor, based on the weighted least squares(WLS) method, a weighted fusion measurement equation is obtained, and it together with the state equation to constitute a equivalent weighted measurement fusion system. Based on the on-line identification of the moving average(NIA) innovation model parameters for weighted measurement fusion sysrem, using the modem time series analysis method, a self-tuning weighted measurement fusion Kalman filter is presented. It is proved that it converges to globally optimal measurement 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.

引用

页码：1586 / +

页数：2