LAUSR

This paper proposes an iteratively re-weighted importance kernel Bayes filter (IRe-KBF) method for handling high-dimensional or complex data in Bayesian filtering problems. This innovative approach incorporates importance weights and an iterative re-weighting scheme inspired by iteratively re-weighted Least Squares (IRLS) to enhance the robustness and accuracy of Bayesian inference. The proposed method does not require explicit specification of prior and likelihood distributions; instead, it learns the kernel mean representations from training data. Experimental results demonstrate the superior performance of this method over traditional KBF methods on high-dimensional datasets.