mlsanityData to 7 Oct 2026

AI-Aided Kalman Filters

arXiv 2410.12289 ↗First posted 16 Oct 2024cs.LG cs.SY eess.SPcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 32 of its 81 references that are in the map. First posted on arXiv 16 Oct 2024.

Builds on

32 of 81 references.

References · 32

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  3. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  4. 2021Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layerscited by 607
  5. 2016Structured Inference Networks for Nonlinear State Space Modelscited by 182
  6. 2015Deep Kalman Filterscited by 163
  7. 2020Model-Based Deep Learningcited by 136
  8. 2021KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamicscited by 128
  9. 2016Sequential Neural Models with Stochastic Layerscited by 115
  10. 2019A Survey of Deep Learning Applications to Autonomous Vehicle Controlcited by 101
  11. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  12. 2019Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spacescited by 64
20 more
  1. 2022Model-Based Deep Learning: On the Intersection of Deep Learning and Optimizationcited by 50
  2. 2020KFNet: Learning Temporal Camera Relocalization using Kalman Filteringcited by 43
  3. 2017Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularizationcited by 36
  4. 2019Combining Generative and Discriminative Models for Hybrid Inferencecited by 34
  5. 2021Data-Driven MPC for Quadrotorscited by 32
  6. 2020Deep State Space Models for Nonlinear System Identificationcited by 30
  7. 2023DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setupcited by 19
  8. 2021Unsupervised Learned Kalman Filteringcited by 17
  9. 2019Model structures and fitting criteria for system identification with neural networkscited by 16
  10. 2023Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signalscited by 16
  11. 2022Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]cited by 13
  12. 2021RTSNet: Learning to Smooth in Partially Known State-Space Models (Preprint)cited by 11
  13. 2023Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filtercited by 10
  14. 2023Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptationcited by 9
  15. 2021Improved Initialization of State-Space Artificial Neural Networkscited by 8
  16. 2021Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filteringcited by 8
  17. 2021Neural Augmentation of Kalman Filter with Hypernetwork for Channel Trackingcited by 5
  18. 2022Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filteringcited by 5
  19. 2022Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Tradingcited by 2
  20. 2024Nonlinear Kalman Filtering based on Self-Attention Mechanism and Lattice Trajectory Piecewise Linear Approximationcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Ensemble Kalman filter in latent space using a variational autoencoder paircited by 5
  2. 2025Uncertainty Propagation Networks for Neural Ordinary Differential Equationscited by 1
  3. 2025Neural Augmented Kalman Filters for Road Network assisted GNSS positioningcited by 0