As dynamic systems continue to exhibit a trend towards increased scale and complexity,the Bayesian fil-tering based state estimation for dynamic systems faces a series of new challenges.With the increasing prominence and new potential of deep learning in areas such as feature extraction and pattern recognition,research on combina-tion of deep learning and classical Bayesian filtering is emerging.In this paper,we present a systematic review of application cases of Bayesian filtering methods that integrate deep learning in different domains,aiming to analyze the limitations and common challenges of Bayesian filtering in various types of dynamic systems.In view of this,we summarize several categories of uncertainty problems in the existing Bayesian filtering.From the perspective of deep learning,these problems are classified into two fundamental problems:Feature extraction and parameter iden-tification.Furthermore,we introduce the solutions provided by deep learning for Bayesian filtering.Additionally,we categorize and organize two specific approaches that combine Bayesian filtering with deep learning,that is,deep Kalman filtering and adaptive Kalman filtering with deep learning.Finally,by considering the advantages of both deep learning and Bayesian filtering methods,we discuss open questions and future research directions for Bayesian filtering with deep learning.
Deep learningBayesian filteringKalman filteringstate estimationstate-space model
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