Approach to frequency estimation in self-mixing interferometry: multiple signal classification

Nikolic, Milan, Jovanovic, Dejan P., Lim, Yah Leng, Bertling, Karl, Taimre, Thomas and Rakic, Aleksandar D. (2013) Approach to frequency estimation in self-mixing interferometry: multiple signal classification. Applied Optics, 52 14: 3345-3350. doi:10.1364/AO.52.003345

Author Nikolic, Milan
Jovanovic, Dejan P.
Lim, Yah Leng
Bertling, Karl
Taimre, Thomas
Rakic, Aleksandar D.
Title Approach to frequency estimation in self-mixing interferometry: multiple signal classification
Journal name Applied Optics   Check publisher's open access policy
ISSN 1559-128X
Publication date 2013-05-10
Year available 2013
Sub-type Article (original research)
DOI 10.1364/AO.52.003345
Volume 52
Issue 14
Start page 3345
End page 3350
Total pages 6
Place of publication Washington, DC, United States
Publisher Optical Society of America
Collection year 2014
Language eng
Abstract Based on the nature of self-mixing signals, we propose the use of the multiple signal classification (MUSIC) algorithm in place of the fast Fourier transform (FFT) for processing signals obtained from self-mixing interferometry (SMI).We apply this algorithm to two representative SMI measurement techniques:range finding and velocimetry. Applying MUSIC to SMI range finding, we find its signal-to-noise ratio performance to be significantly better than that of the FFT, allowing for more robust, longer-range measurement systems. We further demonstrate that MUSIC enables a fundamental change in how SMI Doppler velocity measurement is approached, letting one discard the complex fitting procedure and allowing for a real-time frequency estimation process.
Keyword Doppler velocity measurements
Fundamental changes
Measurement system
Measurement techniques
Multiple signal classification
Multiple signal classification algorithm
Self mixing interferometry
Self-mixing signals
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online 7 May 2013

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
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