KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Guy Revach, Nir Shlezinger, Xiaoyong Ni, Adria Lopez Escoriza, Ruud J.G. Van Sloun, Yonina C. Eldar

Research output: Contribution to journalArticlepeer-review

77 Scopus citations


Real-time state estimation of dynamical systems is a fundamental task in signal processing and control. 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 numerically demonstrate that KalmanNet overcomes nonlinearities and model mismatch, outperforming classic filtering methods operating with both mismatched and accurate domain knowledge.

Original languageEnglish
Pages (from-to)1532-1547
Number of pages16
JournalIEEE Transactions on Signal Processing
StatePublished - 11 Mar 2022


  • Data models
  • Heuristic algorithms
  • Kalman filters
  • Mathematical models
  • Numerical models
  • Real-time systems
  • Task analysis

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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