Memory-attenuated least square filtering and its application

被引：0

作者：

Lu, Ping ^{[1
]}

Zhao, Long ^{[1
]}

Chen, Zhe ^{[1
]}

机构：

[1] BeiHang Univ, Sch Automat Sci & Elect Engn, Beijing 100083, Peoples R China

来源：

WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年

关键词：

integrated navigation system; inertial navigation system; double-star system; memory-attenuated adaptive filtering;

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：

In order to deal with the problem in which the conventional Kalman filtering may be instable or divergent when noise statistics is unknown, a new adaptive filtering is presented, which is defined as Memory-Attenuated Least Square Filtering (MALSF). The error covariance is multiplied by a decay factor to avoid the divergence and an adaptive estimation for decay factor is developed, and a recursive algorithm based on least square filtering is presented. The descriptions of the noise statistics are not required. This algorithm is simple and has the adaptability. MALSF is applied to INS/DS integrated navigation system. Simulation results show that the proposed algorithm has adaptability and has better estimation accuracy than the conventional Kalman filtering and the least square filtering when noise statistics information is unknown.

引用

页码：1483 / +

页数：2