Signed Cumulative Distribution Transform for Parameter Estimation of 1-D Signals

• URL: http://arxiv.org/abs/2207.07989v1
• Date: Sat, 16 Jul 2022 17:44:29 GMT
• Title: Signed Cumulative Distribution Transform for Parameter Estimation of 1-D Signals
• Authors: Sumati Thareja, Gustavo Rohde, Rocio Diaz Martin, Ivan Medri, and Akram Aldroubi
• Abstract summary: The method builds upon signal estimation using the cumulative distribution transform (CDT) originally introduced for positive distributions. We show that Wasserstein-type distance minimization can be performed simply using linear least squares techniques in SCDT space for arbitrary signal classes.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We describe a method for signal parameter estimation using the signed cumulative distribution transform (SCDT), a recently introduced signal representation tool based on optimal transport theory. The method builds upon signal estimation using the cumulative distribution transform (CDT) originally introduced for positive distributions. Specifically, we show that Wasserstein-type distance minimization can be performed simply using linear least squares techniques in SCDT space for arbitrary signal classes, thus providing a global minimizer for the estimation problem even when the underlying signal is a nonlinear function of the unknown parameters. Comparisons to current signal estimation methods using $L_p$ minimization shows the advantage of the method.

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