- 期刊
- OpenAccess
Improved Robust Huber-based Extended Kalman Filtering
並列摘要
This paper presents an improved version of the Huber-based extended Kalman filter (IHEKF) using the Huber's M-estimation methodology, in which the nonlinear measurement model is directly used in the nonlinear regression equation in place of the linear or statistically linear approximation. The proposed filtering approach is robust with respect to deviations in the traditionally assumed Gaussian measurement error probability distributions and has the better estimation accuracy than that of the Huber-based extended Kalman filter (HEKF). This filter algorithm is then applied to a benchmark trajectory estimation problem involving radar range measurements of an atmospheric entry vehicle. Simulation results demonstrate the superior performance of the proposed filter as compared to the HEKF and typical EKF algorithms in the presence of non-Gaussian random measurement errors.
延伸閱讀
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