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DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Bayesian Setup
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0001-6612-6923
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0003-0166-1356
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
2023 (English)In: European Signal Processing Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 870-874Conference paper, Published paper (Refereed)
Abstract [en]

We propose DANSE - a data-driven non-linear state estimation method. DANSE provides a closed-form posterior of the state of a model-free process, given linear measurements of the state in a Bayesian setup, like the celebrated Kalman filter (KF). Non-linear dynamics of the state are captured by data-driven recurrent neural networks (RNNs). The training of DANSE combines maximum-likelihood and gradient-descent in an unsupervised framework, i.e. only measurement data and no process data are required. Using simulated linear and non-linear process models, we demonstrate that DANSE - without knowledge of the process model - provides competitive performance against model-based approaches such as KF, unscented KF (UKF), extended KF (EKF), and a hybrid approach such as KalmanNet.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. p. 870-874
Keywords [en]
deep learning, neural networks, recurrent neural networks, state estimation, Bayesian networks, Deep neural networks, Gradient methods, Maximum likelihood estimation, Bayesian, Closed form, Data driven, Linear state estimation, Model free, Neural-networks, Nonlinear state, Process-models, State estimation methods
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-346076DOI: 10.23919/EUSIPCO58844.2023.10289946Scopus ID: 2-s2.0-85165166616ISBN: 9789464593600 (print)OAI: oai:DiVA.org:kth-346076DiVA, id: diva2:1855653
Conference
31st European Signal Processing Conference, EUSIPCO 2023, 4 September 2023 through 8 September 2023
Note

QC 20240502

Available from: 2024-05-02 Created: 2024-05-02 Last updated: 2024-05-02Bibliographically approved

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Ghosh, AnubhabHonore, AntoineChatterjee, Saikat

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