Chakraborty, RK and Joseph, G and Murthy, CR (2024) BAYESIAN LEARNING-BASED KALMAN SMOOTHING FOR LINEAR DYNAMICAL SYSTEMS WITH UNKNOWN SPARSE INPUTS. In: UNSPECIFIED, pp. 13431-13435.
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Abstract
We consider the problem of jointly estimating the states and sparse inputs of a linear dynamical system using noisy low-dimensional observations. We exploit the underlying sparsity in the inputs using fictitious sparsity-promoting Gaussian priors with unknown variances (as hyperparameters). We develop two Bayesian learning-based techniques to estimate states and inputs: sparse Bayesian learning and variational Bayesian inference. Through numerical simulations, we illustrate that our algorithms outperform the conventional Kalman filtering based algorithm and other state-of-the-art sparsity-driven algorithms, especially in the low-dimensional measurement regime. © 2024 IEEE.
Item Type: | Conference Paper |
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Publication: | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Additional Information: | The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc. |
Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
Date Deposited: | 20 Aug 2024 05:19 |
Last Modified: | 20 Aug 2024 05:19 |
URI: | http://eprints.iisc.ac.in/id/eprint/85480 |
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