Unsupervised Learned Kalman Filtering

Guy Revach, Nir Shlezinger, Timur Locher, Xiaoyong Ni, Ruud J.G. van Sloun, Yonina C. Eldar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations


In this paper we adapt KalmanNet, which is a recently proposed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF), to learn its mapping in an unsupervised manner, i.e., without requiring ground-truth states. The unsupervised adaptation is achieved by exploiting the hybrid model-based/data-driven architecture of KalmanNet, which internally predicts the next observation as the KF does. These internal features are then used to compute the loss rather than the state estimate at the output of the system. With the capability of unsupervised learning, one can use KalmanNet not only to track the hidden state, but also to adapt to variations in the state space (SS) model. We numerically demonstrate that when the noise statistics are unknown, unsupervised KalmanNet achieves a similar performance to KalmanNet with supervised learning. We also show that we can adapt a pre-trained KalmanNet to changing SS models without providing additional data thanks to the unsupervised capabilities.

Original languageEnglish
Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9789082797091
StatePublished - 18 Oct 2022
Event30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: 29 Aug 20222 Sep 2022

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference30th European Signal Processing Conference, EUSIPCO 2022


  • Kalman filter
  • unsupervised learning

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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