title: Approach to frequency estimation in self-mixing interferometry: multiple signal classification.
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.
authors.affiliation: School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.
authors.forename: Milan
authors.initials: M
authors.lastname: Nikolić
authors.forename: Dejan P
authors.initials: DP
authors.lastname: Jovanović
authors.forename: Yah Leng
authors.initials: YL
authors.lastname: Lim
authors.forename: Karl
authors.initials: K
authors.lastname: Bertling
authors.forename: Thomas
authors.initials: T
authors.lastname: Taimre
authors.forename: Aleksandar D
authors.initials: AD
authors.lastname: Rakić