Robust self-adaptive Kalman filter with application in target tracking
學年 111
學期 1
出版（發表）日期 2022-08-30
作品名稱 Robust self-adaptive Kalman filter with application in target tracking
作品名稱（其他語言）
著者 Yi-Wei Chen; Ken-Ming Tu
單位
出版者
著錄名稱、卷期、頁數 Measurement and Control 55(9-10)
摘要 Kalman filter has been applied extensively to the target tracking. The estimation performance of Kalman filter is closely resulted by the quality of prior information about the process noise covariance (Q) and the measurement noise covariance (R). Therefore, the development of adaptive Kalman filter is mainly to reduce the estimation errors produced by the uncertainty of Q and R. In this paper, the proposed self-adaptive Kalman filter algorithm has solved the problems of covariance-matching method about the determination of the width of the window and the addition of storage burden and that can update Q and R simultaneously. Simulation results confirm that the proposed method outperforms the traditional Kalman filter and has the better estimation performance than the other two adaptive Kalman filters in the target tracking. The developed filtering algorithm has the following characteristics: high robustness, low computing load, easy operation and tuning Q, R simultaneously.
關鍵字 Adaptive Kalman filter; process noise covariance; measurement noise covariance; target tracking.
語言 en
ISSN 0020-2940
期刊性質 國外
收錄於 SSCI
產學合作
通訊作者 Yi-Wei Chen
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128576 )