Robust Kalman filtering with generalized Gaussian measurement noise

被引：8

作者：

Niehsen, W ^{[1
]}

机构：

[1] Robert Bosch GmbH, Corp Res & Dev, D-31132 Hildesheim, Germany

来源：

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2002年 / 38卷 / 04期

关键词：

D O I：

10.1109/TAES.2002.1145765

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

A recursive state estimator based on adaptive generalized Gaussian approximation of the innovations sequence probability density function is constructed. The proposed state estimator is computationally efficient and robust in the case of heavy-tailed measurement noise. Compared with standard Kalman filtering, significant improvements with respect to stationary mean square error and rate of convergence are achieved.

引用

页码：1409 / 1412

页数：4

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