Huber‐based Unscented Kalman Filters with the q ‐gradient
Shiyuan Wang, Wenjie Zhang, Chao Yin, Yali Feng · IET Science Measurement & Technology · 2017
This study presents the Huber‐based unscented Kalman filters with the q ‐gradient (HUKF‐Q). As an extension of the classical gradient vector based on the concept of Jackson's derivative, the q ‐gradient can be utilised to improve the optimisation performance of the Huber method, significantly. Combining the Huber method based on the q ‐gradient into state estimation based on the unscented transformation, generates the novel HUKF‐Q. The Cramér–Rao lower bound is introduced as a performance measure metric. Compared with the conventional HUKF, the proposed HUKF‐Q can achieve better filtering accuracy and robustness. In addition, the impact of the tuning parameter q on the filtering performance is discussed by simulations. Simulations on the two examples of univariate non‐stationary growth model and bearings only tracking model, confirm the superior performance of the proposed HUKF‐Q.