Mathematical Optimization-Based Period Estimation with Outliers and Missing Observations

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Bibliographische Detailangaben
Hauptverfasser:	Puech, Romain, Gouldieff, Vincent
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Optimization and Control Signal Processing
Online-Zugang:	Acceder al recurso
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author	Puech, Romain Gouldieff, Vincent
author_facet	Puech, Romain Gouldieff, Vincent
contents	We consider the frequency estimation of periodic signals using noisy time-of-arrival (TOA) information with missing (sparse) data contaminated with outliers. We tackle the problem from a mathematical optimization standpoint, formulating it as a linear regression with an unknown increasing integer independent variable and outliers. Assuming an upper bound on the variance of the noise, we derive an online, parallelizable, near-CRLB optimization-based algorithm amortized to a linear complexity. We demonstrate the outstanding robustness of our algorithm to noise and outliers by testing it against diverse randomly generated signals. Our algorithm handles outliers by design and yields precise estimations even with up to 20% of contaminated data.
format	Preprint
id	arxiv_https___arxiv_org_abs_2409_00526
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Mathematical Optimization-Based Period Estimation with Outliers and Missing Observations Puech, Romain Gouldieff, Vincent Optimization and Control Signal Processing We consider the frequency estimation of periodic signals using noisy time-of-arrival (TOA) information with missing (sparse) data contaminated with outliers. We tackle the problem from a mathematical optimization standpoint, formulating it as a linear regression with an unknown increasing integer independent variable and outliers. Assuming an upper bound on the variance of the noise, we derive an online, parallelizable, near-CRLB optimization-based algorithm amortized to a linear complexity. We demonstrate the outstanding robustness of our algorithm to noise and outliers by testing it against diverse randomly generated signals. Our algorithm handles outliers by design and yields precise estimations even with up to 20% of contaminated data.
title	Mathematical Optimization-Based Period Estimation with Outliers and Missing Observations
topic	Optimization and Control Signal Processing
url	https://arxiv.org/abs/2409.00526

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