The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
Open accessAbstract
State estimation of dynamical systems in real-time is a fundamental task in signal processing. 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 demonstrate numerically that KalmanNet overcomes non-linearities and model mismatch, outperforming classic filtering methods operating with both mismatched and accurate domain knowledge.
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Adam: A Method for Stochastic Optimization
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
A New Approach to Linear Filtering and Prediction Problems
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Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
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Cited by
Cheng, S., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., et al. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123537 · Cited by 303
A comparative review on multi-modal sensors fusion based on deep learning
Tang, Q., Liang, J., & Zhu, F. (2023) · Signal Processing · doi:10.1016/j.sigpro.2023.109165 · Cited by 280
Enhanced Deep Learning-Aided Kalman Filter Based on KalmanNet for Robust Nonlinear State Estimation
Fu, T., Mao, G., Wang, Z., & Wang, K. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3672319 · Cited by 6
Focus Your Attention (with Adaptive IIR Filters)
Lutati, S., Zimerman, I., & Wolf, L. (2023) · arXiv · doi:10.48550/arxiv.2305.14952
Metadata
- Authors
- Revach, G., Shlezinger, N., Ni, X., Escoriza, A. L., van Sloun, R. J. G., & Eldar, Y. C.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2022
- Volume
- 70
- Pages
- 1532-1547
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W3183282730
- Cited by
- 599
- References
- 76
- FWCI
- 34.25
- Type
- journal article
- Language
- en
- Open access
- green
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- Revach, G., Shlezinger, N., Ni, X., Escoriza, A. L., van Sloun, R. J. G., & Eldar, Y. C. (2022). KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2022.3158588
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