A Bayes filter is a widely used estimation algorithm, but it has inherent limitations. Performance can degrade when the dynamics are highly nonlinear or when the probability distribution of the state is unknown. To mitigate these issues, machine learning (ML) techniques have been incorporated into many Bayes filters, due to their advantage of being able to map between the input and the output without explicit instructions. In this review, we reviewed 90 papers that proposed the use of ML techniques with Bayes filters to improve estimation performance. This review provides an overview of Bayes filters with ML techniques, categorised according to the role of ML, remaining challenges and research gaps. In the concluding section of this review, we point out directions for future research.

中文翻译：

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使用机器学习技术回顾贝叶斯滤波器及其应用


贝叶斯滤波器是一种广泛使用的估计算法，但它有其固有的局限性。当动态高度非线性或状态的概率分布未知时，性能可能会降低。为了缓解这些问题，机器学习 （ML） 技术已被纳入许多贝叶斯滤波器中，因为它们的优势在于能够在没有明确指令的情况下在输入和输出之间进行映射。在这篇综述中，我们回顾了 90 篇论文，这些论文提议使用 ML 技术和贝叶斯滤波器来提高估计性能。本综述概述了使用 ML 技术的贝叶斯滤波器，根据 ML 的作用、仍然存在的挑战和研究差距进行分类。在这篇综述的结论部分，我们指出了未来研究的方向。