@inproceedings{49ce3c446eac4090a3abeb71f27a28a6,
title = "Kalmannet: Data-Driven Kalman Filtering",
abstract = "The Kalman filter (KF) is a celebrated signal processing algorithm, implementing optimal state estimation of dynamical systems that are well represented by a linear Gaussian state-space model. The KF is model-based, and therefore relies on full and accurate knowledge of the underlying model. We present KalmanNet, a hybrid data-driven/model-based filter that does not require full knowledge of the underlying model parameters. KalmanNet is inspired by the classical KF flow and implemented by integrating a dedicated and compact neural network for the Kalman gain computation. We present an offline training method, and numerically illustrate that KalmanNet can achieve optimal performance without full knowledge of the model parameters. We demonstrate that when facing inaccurate parameters KalmanNet learns to achieve notably improved performance compared to KF.",
author = "Guy Revach and Nir Shlezinger and \{van Sloun\}, \{Ruud J. G\} and Eldar, \{Yonina C\}",
note = "Publisher Copyright: {\textcopyright}2021 IEEE.; 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 ; Conference date: 06-06-2021 Through 11-06-2021",
year = "2021",
doi = "10.1109/ICASSP39728.2021.9413750",
language = "الإنجليزيّة",
isbn = "978-1-7281-7606-2",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "3905--3909",
booktitle = "2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Proceedings",
}