KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Guy Revach (Corresponding author), Nir Shlezinger, Xiaoyong Ni, Adrià López Escoriza, Ruud J.G. van Sloun, Yonina C. Eldar

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

211 Citaten (Scopus)
1055 Downloads (Pure)

Samenvatting

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.

Originele taal-2Engels
Artikelnummer9733186
Pagina's (van-tot)1532-1547
Aantal pagina's16
TijdschriftIEEE Transactions on Signal Processing
Volume70
DOI's
StatusGepubliceerd - mrt. 2022

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Publisher Copyright:
IEEE

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