Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models-Reference-Cited by-同舟云学术

Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models

Published:2022-05-23 Issue: Volume: Page:
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Container-title:ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Klein Itzik¹,Revach Guy²,Shlezinger Nir³,Mehr Jonas E.²,van Sloun Ruud J. G.⁴,Eldar Yonina. C.⁵

Affiliation:

1. University of Haifa,Department of Marine Technologies,Israel

2. ETH Zürich,Institute for Signal and Information Processing (ISI), D-ITET

3. Ben-Gurion University of the Negev,School of ECE,Beer Sheva,Israel

4. Eindhoven University of Technology, and with Phillips Research,EE Dpt.,Eindhoven,The Netherlands

5. Weizmann Institute of Science,Faculty of Math and CS,Rehovot,Israel

Publisher

IEEE

Link

Reference23 articles.

1. Hands-on Bayesian neural networks–a tutorial for deep learning users;jospin,2020

2. Deep variational Bayes filters: Unsupervised learning of state space models from raw data;karl,2016

4. Variational sequential Monte Carlo;naesseth;International Conference on Artificial Intelligence and Statistics,2018

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