Cauchy kernel-based maximum correntropy Kalman filter. Issue 16 (9th December 2020)

• Record Type: Journal Article
• Authors: Wang, Jiongqi; Lyu, Donghui; He, Zhangming; Zhou, Haiyin; Wang, Dayi
• Is Part Of: International journal of systems science. Volume 51:Issue 16(2020)
• Page Start: 3523
• Page End: 3538
• Publication Date: 2020-12-09
• Subjects: Kalman filter -- maximum correntropy -- Cauchy kernel function -- CKKF; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2020.1817614
• Languages: English
• ISSNs: 0020-7721

Abstract

Abstract : Non-Gaussian noise processing is a difficult and hot spot in the study of filters. A currently effective method to deal with non-Gaussian noise is replacing the minimum mean square error criterion with the maximum correntropy criterion. Based on the maximum correntropy criterion, maximum correntropy Kalman filter, which usually uses the Gaussian kernel function to define the distance between vectors, is developed. However, when the non-Gaussian noise is multi-dimensional, maximum correntropy Kalman filter tends to break down due to the appearance of singular matrices. To overcome the drawback, a novel filter named Cauchy kernel-based maximum correntropy Kalman filter is proposed, which utilises the Cauchy kernel function to define the distance between vectors. Due to the insensitive feature to the kernel bandwidth and thick-tailed characteristic of the Cauchy kernel function, Cauchy kernel-based maximum correntropy Kalman filter can effectively avoid filter faults and has a better stability. Simulation results demonstrate the excellent performance of the proposed algorithm by comparing it with other conventional methods, such as Kalman filter, ideal Kalman filter, Huber-based filter, Gaussian sum filter and maximum correntropy Kalman filter.