BAYESIAN LEARNING-BASED KALMAN SMOOTHING FOR LINEAR DYNAMICAL SYSTEMS WITH UNKNOWN SPARSE INPUTS

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
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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