Kalmannet: Data-Driven Kalman Filtering
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Date
2021
Publication Type
Conference Paper
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yes
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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.
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Publication status
published
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Book title
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Journal / series
Volume
Pages / Article No.
3905 - 3909
Publisher
IEEE
Event
2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021)
Edition / version
Methods
Software
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Date created
Subject
Kalman filter; deep learning; model-based
Organisational unit
03568 - Loeliger, Hans-Andrea / Loeliger, Hans-Andrea