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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 52, Iss. 14 — May. 10, 2013
  • pp: 3345–3350

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

Milan Nikolić, Dejan P. Jovanović, Yah Leng Lim, Karl Bertling, Thomas Taimre, and Aleksandar D. Rakić  »View Author Affiliations


Applied Optics, Vol. 52, Issue 14, pp. 3345-3350 (2013)
http://dx.doi.org/10.1364/AO.52.003345


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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.

© 2013 Optical Society of America

OCIS Codes
(280.3420) Remote sensing and sensors : Laser sensors
(280.4788) Remote sensing and sensors : Optical sensing and sensors

ToC Category:
Remote Sensing and Sensors

History
Original Manuscript: November 21, 2012
Manuscript Accepted: March 29, 2013
Published: May 7, 2013

Citation
Milan Nikolić, Dejan P. Jovanović, Yah Leng Lim, Karl Bertling, Thomas Taimre, and Aleksandar D. Rakić, "Approach to frequency estimation in self-mixing interferometry: multiple signal classification," Appl. Opt. 52, 3345-3350 (2013)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-52-14-3345

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