[2407.07368] Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2407.07368 (eess) [Submitted on 10 Jul 2024 ( v1 ), last revised 25 Apr 2026 (this version, v8)] Title: Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements Authors: Anubhab Ghosh , Yonina C. Eldar , Saikat Chatterjee View a PDF of the paper titled Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements, by Anubhab Ghosh and 2 other authors View PDF HTML (experimental) Abstract: We consider data-driven Bayesian state estimation from compressed measurements (BSCM) of a model-free process. The dimension of the temporal measurement vector is lower than that of the temporal state vector to be estimated, leading to an under-determined inverse problem. The underlying dynamical model of the state's evolution is unknown for a `model-free process.' Hence, it is difficult to use traditional model-driven methods, for example, Kalman and particle filters. Instead, we consider data-driven methods. We experimentally show that two existing unsupervised learning-based data-driven methods fail to address the BSCM problem in a model-free process. The methods are -- data-driven nonlinear state estimation (DANSE) and deep Markov model (DMM). While DANSE provides good predictive/forecasting performance to model the temporal measurement data as a time series, its unsupervised learning lacks suitable regularization for tackling the BSCM task. We then propose a semi-supervised learning approach and develop a semi-supervised learning-based DANSE method, referred to as SemiDANSE. In SemiDANSE, we use a large amount of unlabelled data along with a limited amount of labelled data, i.e., pairwise measurement-and-state data, which provides the desired regularization. Using {benchmark chaotic dynamical systems}, we {empirically} show that the data-driven SemiDANSE provides competitive state estimation performance for BSCM {using a handful of different measurement systems}, against a hybrid method called KalmanNet and two model-driven methods (extended Kalman filter and unscented Kalman filter) that know the dynamical models exactly. Comments: 14 pages, 14 figures, under review in IEEE Transactions on Signal Processing Subjects: Signal Processing (eess.SP) ; Machine Learning (cs.LG) Cite as: arXiv:2407.07368 [eess.SP] (or arXiv:2407.07368v8 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2407.07368 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Anubhab Ghosh [ view email ] [v1] Wed, 10 Jul 2024 05:03:48 UTC (1,721 KB) [v2] Tue, 14 Jan 2025 07:28:06 UTC (1,741 KB) [v3] Fri, 4 Apr 2025 13:38:45 UTC (1,756 KB) [v4] Fri, 23 May 2025 16:59:15 UTC (1,822 KB) [v5] Mon, 26 May 2025 05:10:07 UTC (1,822 KB) [v6] Wed, 4 Jun 2025 13:46:38 UTC (1,725 KB) [v7] Sun, 1 Feb 2026 14:19:05 UTC (3,999 KB) [v8] Sat, 25 Apr 2026 17:29:21 UTC (4,008 KB) Full-text links: Access Paper: View a PDF of the paper titled Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements, by Anubhab Ghosh and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2024-07 Change to browse by: cs cs.LG eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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