This paper proposes a Kalman filter framework for signal denoising that simultaneously utilizes conventional linear time-invariant (LTI) filtering and total variation (TV) denoising. In this approach, the desired signal is considered to be a mixture of two distinct components: a band-limited (e.g., low-frequency component, high-frequency component) signal and a sparse-derivative signal. An iterative Kalman filter/smoother approach is formulated where zero-phase LTI filtering is used to estimate the band-limited signal and TV denoising is used to estimate the sparse-derivative signal.

A Kalman Filter Framework for Simultaneous LTI Filtering and Total Variation Denoising / A. Kheirati Roonizi, I.W. Selesnick. - In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - ISSN 1053-587X. - 70:(2022), pp. 4543-4554. [10.1109/TSP.2022.3203852]

A Kalman Filter Framework for Simultaneous LTI Filtering and Total Variation Denoising

A. Kheirati Roonizi
Primo
;
2022

Abstract

This paper proposes a Kalman filter framework for signal denoising that simultaneously utilizes conventional linear time-invariant (LTI) filtering and total variation (TV) denoising. In this approach, the desired signal is considered to be a mixture of two distinct components: a band-limited (e.g., low-frequency component, high-frequency component) signal and a sparse-derivative signal. An iterative Kalman filter/smoother approach is formulated where zero-phase LTI filtering is used to estimate the band-limited signal and TV denoising is used to estimate the sparse-derivative signal.
TV; Filtering; Noise reduction; Optimization; Low-pass filters; Linear systems; Noise measurement; Sparse derivative signal; zero-phase filters; band-limited signal; total variation; Kalman filter; smoother
Settore INF/01 - Informatica
2022
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/952837
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