AI-Aided Kalman Filters

2024-10-16

arXiv.org

AI Summary

Traditional Kalman filtering (KF) suffers from limited state estimation accuracy due to oversimplified state-space models. To address this, we propose an AI-enhanced filtering framework that deeply integrates model-driven and data-driven paradigms. We systematically introduce two novel AI-KF fusion paradigms—task-oriented and state-space-model-oriented—and embed deep neural networks directly into the KF architecture, enabling adaptive modeling of unknown dynamics while preserving physical interpretability. Our method supports partial state-space modeling and end-to-end joint training. Experiments across diverse nonlinear and time-varying systems demonstrate significant improvements in tracking accuracy and robustness over conventional approaches. Furthermore, we fully open-source the implementation, establishing the first reproducible benchmark and design paradigm for AI-augmented filtering.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Enhance Kalman filters using AI for better state estimation.
• Fuse deep neural networks with Kalman-type filtering techniques.
• Combine model-based and data-driven approaches for dynamic systems.

Innovation

• Fuse DNNs with Kalman filters
• Leverage partial state-space modeling
• Combine model-based and data-driven designs