Adaptive Fast Desensitized Kalman Filter

Adjusting the sensitivity-weighting matrix, which is a key parameter affecting the filtering accuracy in the desensitized Kalman filter (DKF), is still an open problem. To address this issue, a new adaptive fast DKF (AFDKF) algorithm and adaptive fast desensitized extended Kalman filter (AFDEKF) hav...

Full description

Saved in:
Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2024-11, Vol.43 (11), p.7364-7386
Main Authors: Lou, Tai-shan, Chen, Nanhua, Zhao, Liangyu
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive filters
Algorithms
Circuits and Systems
Cost analysis
Cost function
Desensitization
Design
Electrical Engineering
Electronics and Microelectronics
Engineering
Extended Kalman filter
Instrumentation
Kalman filters
Orthogonality
Parameter sensitivity
Signal processing
Signal,Image and Speech Processing
Weighting
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Adjusting the sensitivity-weighting matrix, which is a key parameter affecting the filtering accuracy in the desensitized Kalman filter (DKF), is still an open problem. To address this issue, a new adaptive fast DKF (AFDKF) algorithm and adaptive fast desensitized extended Kalman filter (AFDEKF) have been proposed. The fast filters have an adaptive factor that enables them to adjust the sensitivity-weighting matrix based on the orthogonality principle of measurement residuals. This adaptive factor is calculated by using the corresponding process and measurement information. Then, a new desensitized cost function with an adaptive factor is designed. An analytical gain is obtained by minimizing this cost function to reduce computation cost. The performance of the AFDKF and AFDEKF algorithms are demonstrated using two numerical examples.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-024-02801-3