KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Guy Revach, Nir Shlezinger, Xiaoyong Ni, Adria Lopez Escoriza, Ruud J. G. van Sloun +1 more
7/21/2021
eess.SPcs.LGstat.ML

Official code: KalmanNet/KalmanNet_TSP (460 stars, last commit 2024-02-12).

Abstract

State estimation of dynamical systems in real-time is a fundamental task in signal processing. For systems that are well-represented by a fully known linear Gaussian state space (SS) model, the celebrated Kalman filter (KF) is a low complexity optimal solution. However, both linearity of the underlying SS model and accurate knowledge of it are often not encountered in practice. Here, we present KalmanNet, a real-time state estimator that learns from data to carry out Kalman filtering under non-linear dynamics with partial information. By incorporating the structural SS model with a dedicated recurrent neural network module in the flow of the KF, we retain data efficiency and interpretability of the classic algorithm while implicitly learning complex dynamics from data. We demonstrate numerically that KalmanNet overcomes non-linearities and model mismatch, outperforming classic filtering methods operating with both mismatched and accurate domain knowledge.

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Code Implementations(1)

KalmanNet/KalmanNet_TSPOfficial100%

code for KalmanNet

460116PythonJul 15, 20212 years ago

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Cite this paper

@article{revach2021kalmannet,
  title  = {KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics},
  author = {Guy Revach and Nir Shlezinger and Xiaoyong Ni and Adria Lopez Escoriza and Ruud J. G. van Sloun and Yonina C. Eldar},
  year   = {2021},
  eprint = {2107.10043},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2107.10043}
}

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