A novel resampling-free update framework-based cubature Kalman filter for robust estimation

• Published in: Signal processing Vol. 221; p. 109507
• Main Authors: Shao, Jianbo, Zhang, Ya, Yu, Fei, Fan, Shiwei, Sun, Qian, Chen, Wu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024
• Subjects: Cubature Kalman filter; Maximum correntropy criterion; Resampling-free update; Maximum correntropy criterion; Cubature Kalman filter; Resampling-free update
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2024.109507

The resampling-free update (RFU) framework avoids discarding the higher-order moment information of the state probability distribution in Gaussian approximation filters. Still, it suffers from the problem of numerical instability and estimation optimality being corrupted caused by non-closed mapping without Gaussian reconstruction. This study proposes a novel robust RFU framework-based cubature Kalman filter. The maximum correntropy criterion is adopted as the optimization cost to exploit the non-Gaussian moments caused by non-closed mapping in RFU. An RFU update is reconstructed based on the square root of a posterior error matrix to improve the numerical stability. In addition, the periodic resampling operation is implemented to mitigate the non-Gaussianity while keeping higher-order moments. The illustrative example demonstrates that the proposed method can improve the estimation robustness and consistency of the RFU framework compared to other state-of-the-art RFU-based filters. •An RFU update is designed based on the square root of a posterior error matrix.•MCC is adopted as optimization criterion to exploit non-Gaussian moments of RFU.•The theoretical performance of the RFU filters is analyzed.