q-Renyi Kernel Functioned Kalman Filter for Land Vehicle Navigation

作     者：Xing, Jiangnan Jiang, Tao Li, Yingsong 

作者机构：Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS》 (IEEE Trans. Circuits Syst. Express Briefs)

年 卷 期：2022年第69卷第11期

页      面：4598-4602页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Shanghai Aerospace Science and Technology Innovation Fund [SAST2019-002] Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, China 

主　　题：Kernel Kalman filters Estimation Signal processing algorithms Mathematical models Covariance matrices Computational complexity Kalman filter q-Renyi kernel function non-Gaussian measurement noise large outlier 

摘      要：In this brief, a q-Renyi kernel functioned Kalman filter (qRKFKF) is proposed based on the q-Renyi kernel function which is to provide better flexibility and performance in non-Gaussian environment. The concrete realization of the proposed qRKFKF are created and analyzed in detail, and its performance is presented and discussed. Three examples are carried out to verify the performance of the proposed qRKFKF for land vehicle navigation via numerical simulations: large outliers in non-Gaussian noise, alpha-stable noise and diverse noise distributions that combines Gaussian and Laplace noises. Compared with Kalman filter (KF), Maximum Correntropy Kalman Filter (MCKF) and minimum error entropy KF (MEE-KF), the qRKFKF is superior to others for combating non-Gaussian noises.