Covariance Intersection Fusion Robust Steady-State Kalman Filter for Multi-Sensor Systems with Unknown Noise Variances

被引：0

作者：

Qi, Wenjuan ^{[1
,2
]}

Zhang, Peng ^{[1
,2
]}

Feng, Wenqing ^{[1
,2
]}

Deng, Zili ^{[1
,2
]}

机构：

[1] Heilongjiang Univ, Dept Automat, XueFu Rd 74, Harbin 150080, Heilongjiang, Peoples R China

[2] Heilongjiang Univ, Elect & Engn Coll, Harbin 150080 130, Heilongjiang, Peoples R China

来源：

PROCEEDINGS OF 2013 CHINESE INTELLIGENT AUTOMATION CONFERENCE: INTELLIGENT AUTOMATION | 2013年 / 254卷

关键词：

Multi-sensor data fusion; Covariance intersection fusion; Robust Kalman filter; Uncertain noise variances;

D O I：

10.1007/978-3-642-38524-7_95

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 multi-sensor systems with uncertainties of noise variances, a local robust steady-state Kalman filter with conservative upper bounds of unknown noise variances is presented. Based on the Lyapunov equation, its robustness is proved. Further, the covariance intersection (CI) fusion robust steady-state Kalman filter is presented. It is proved that its robust accuracy is higher than that of each local robust Kalman filter. A Monte-Carlo simulation example shows its correctness and effectiveness.

引用

页码：853 / 860

页数：8

共 42 条

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