Robust maximum correntropy criterion based square-root rotating lattice Kalman filter.

Lattice Kalman filter (LKF) is a nonlinear Kalman filter that utilizes a deterministic sampling method with the advantages of optional sampling points and a flexible balance between computational burden and estimation accuracy. However, the fixed angle of sampling points in LKF can limit the optimality of the selected points. To this end, this paper proposes a novel maximum correntropy square-root rotating lattice Kalman filter (MCSRLKF) to improve the performance of LKF by adjusting the angle of sampling points. In MCSRLKF, a rotation matrix is first constructed to enhance the estimation accuracy of LKF and the optimal rotation angle of sampling points is selected to generate rotating lattice Kalman filter (RLKF). Then, the square-root RLKF (SRLKF) is proposed to enhance the stability and estimation accuracy of RLKF. Due to the utilization of the minimum mean square error criterion in SRLKF, there is a potential for significant performance degradation in non-Gaussian noises. Thus, to enhance the robustness against non-Gaussian noises, the maximum correntropy criterion is applied to SRLKF, generating MCSRLKF. Moreover, the Cramér-Rao lower bound (CRLB) serves as an indicator for assessing the performance of MCSRLKF. Finally, simulations on the nonlinear function model and reentry vehicle tracking model are used to demonstrate that MCSRLKF exhibits excellent filtering accuracy and robustness when dealing with non-Gaussian noises. [ABSTRACT FROM AUTHOR]