Phase estimation using sine fitting with low number of samples is biased in the presence of additive noise

Francisco André Corrêa Alegria

doi:10.30574/gjeta.2023.17.2.0238

Research & review articles are invited for publication in September 2026 (Vol. 28, Issue 3) || Submission: up to 28^th September || Editorial decision: within 48 hrs.

Phase estimation using sine fitting with low number of samples is biased in the presence of additive noise

Francisco André Corrêa Alegria ^*

Instituto de Telecomunicações and Instituto Superior Técnico, Technical University of Lisbon, Portugal.

Research Article

Global Journal of Engineering and Technology Advances, 2023, 17(02), 120–130

Article DOI: 10.30574/gjeta.2023.17.2.0238

DOI url: https://doi.org/10.30574/gjeta.2023.17.2.0238

Publication history

Received on 12 October 2023; revised on 20 November 2023; accepted on 23 November 2021

Abstract

It is demonstrated here that the initial phase estimation of a sine wave corrupted by additive noise using the least squares sine fitting algorithm is not biased, even for low number of samples, contrary to what happens in the case of the amplitude estimation. Monte Carlo simulations are presented which corroborate the conclusion reached. This result is of particular importance when studying the uncertainty of measurement methods based on phase estimation like ultrasonic non‑destructive testing.

Keywords

Sine wave fitting; Uncertainty; ADC; Noise; Bias; Error

Download Article PDF

https://gjeta.com/sites/default/files/fulltext_pdf/GJETA-2023-0238.pdf

Preview Article PDF

How to cite this article

Francisco André Corrêa Alegria. Phase estimation using sine fitting with low number of samples is biased in the presence of additive noise. Global Journal of Engineering and Technology Advances, 2023, 17(2), 120-130. Article DOI: https://doi.org/10.30574/gjeta.2023.17.2.0238

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.

All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.