AI-Aided Kalman Filters

TL;DR

This paper reviews how artificial intelligence, especially deep neural networks, can be integrated with Kalman filters to improve state estimation in dynamic systems, highlighting design strategies, benefits, and challenges.

Contribution

It provides a comprehensive tutorial on AI-Kalman filter fusion, categorizes design approaches, and evaluates their effectiveness through qualitative and quantitative analysis.

Findings

Hybrid AI-KF systems improve tracking accuracy.

Design approaches preserve strengths of both model-based and data-driven methods.

Publicly available code demonstrates the benefits of hybrid designs.

Abstract

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.