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

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.