Adaptively Tuning the Scaling Parameter of the Unscented Kalman Filter

被引：4

作者：

Scardua, Leonardo Azevedo ^{[1
]}

da Cruz, Jose Jaime ^{[1
]}

机构：

[1] Univ Sao Paulo, Automat & Control Lab, Sao Paulo, Brazil

来源：

CONTROLO'2014 - PROCEEDINGS OF THE 11TH PORTUGUESE CONFERENCE ON AUTOMATIC CONTROL | 2015年 / 321卷

关键词：

unscented transform; unscented Kalman filter; scaling parameter; automatic tuning; nonlinear state estimation;

D O I：

10.1007/978-3-319-10380-8_41

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper describes a new approach to adaptively tuning the scaling parameter of the unscented Kalman filter. The proposed algorithm is based on the idea of moment matching and is computationally inexpensive, allowing it to be executed online. Two nonlinear filtering problems are used to numerically compare the performance of the proposed algorithm with the performances of recently published adaptive unscented Kalman filters. An unscented Kalman filter enhanced with the proposed adaptive algorithm outperformed the other adaptive filters in both numerical problems.

引用

页码：429 / 438

页数：10

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