用AI增强卡尔曼滤波，让系统在未知动态下仍能精准追踪。
AI-Aided Kalman Filters
- 将深度神经网络嵌入卡尔曼滤波框架，实现模型与数据联合学习。
- 在部分已知状态空间模型下，融合方法相比纯模型或纯数据方法性能更优。
- 适合需要高精度状态估计的智能系统，如自动驾驶、机器人导航。
卡尔曼滤波（KF）及其变体是信号处理中最具影响力的算法之一，通过简单的状态空间（SS）模型对动态系统进行状态估计，但这些模型可能对真实动态描述粗糙且不准确。新兴的数据驱动人工智能（AI）技术利用深度神经网络（DNN）进行状态估计，具有模型无关性。近期研究展示了将DNN与经典卡尔曼型滤波融合的可行性，构建出能在部分已知动态下自主学习跟踪的系统。本文以教程形式综述了将AI融入卡尔曼型算法的设计方法，涵盖通用与专用的DNN架构，并系统阐述了融合AI工具与KF的技术，按任务导向和状态空间建模导向分类。通过定性和定量分析，验证了各类方法在保留模型驱动与数据驱动优势方面的有效性，代码已公开。同时讨论了融合中的现存挑战与未来方向。
原文摘要 · Abstract (English)
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying dynamics. Emerging data-centric artificial intelligence (AI) techniques tackle these tasks using deep neural networks (DNNs), which are model-agnostic. Recent developments illustrate the possibility of fusing DNNs with classic Kalman-type filtering, obtaining systems that learn to track in partially known dynamics. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms. We review both generic and dedicated DNN architectures suitable for state estimation, and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented. The usefulness of each approach in preserving the individual strengths of model-based KFs and data-driven DNNs is investigated in a qualitative and quantitative study, whose code is publicly available, illustrating the gains of hybrid model-based/data-driven designs. We also discuss existing challenges and future research directions that arise from fusing AI and Kalman-type algorithms.
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