Volume 10, Issue 2 (Journal of Control, V.10, N.2 Summer 2016)                   JoC 2016, 10(2): 55-72 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mohammadi M, Gholizade-Narm H. Adaptation of the Noise Covariance in Extended Kalman Filter Applied on Bearing Only Target Tracking Using Indirect Recursive Method. JoC 2016; 10 (2) :55-72
URL: http://joc.kntu.ac.ir/article-1-386-en.html
1- Shahroud University of Technology
Abstract:   (9135 Views)
This paper proposes a recursive method to determine the process and measurement noise covariance matrix in the extended Kalman filter in application of bearing-only target tracking. One of the requirements of Kalman filters is knowledge of process and measurement noise covariance matrices. If the inappropriate choice of covariance, the filter performance is affected and even there is the possibility of divergence. In this paper, a recursive structure to adapting noise covariance is presented that unlike the conventional methods, instead of direct adapting covariance matrices, based on steepest descent adapting rule structure parameters are adapted. This increases the reliability of the adaptive method and non-negative condition of some of covariance matrix elements to be resolved. To evaluate the performance of proposed method, the bearing-only target tracking scenario is considered. To compare the proposed approach, three adaptive covariance common methods is used that simulation results show that the reliability and efficiency of the proposed method.
Full-Text [PDF 1460 kb]   (8169 Downloads)    
Type of Article: Review paper | Subject: Special
Received: 2016/06/27 | Accepted: 2016/10/26 | Published: 2016/10/29

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Control

Designed & Developed by : Yektaweb