1. A novel resampling-free update framework-based cubature Kalman filter for robust estimation.
- Author
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Shao, Jianbo, Zhang, Ya, Yu, Fei, Fan, Shiwei, Sun, Qian, and Chen, Wu
- Subjects
- *
DISTRIBUTION (Probability theory) , *KALMAN filtering , *SQUARE root , *GAUSSIAN distribution - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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