Adaptive Cancellation of Parasitic Vibrations Affecting a Self-Mixing Interferometric Laser Sensor

Abstract

In this paper, an adaptive method of cancellation of parasitic vibrations is presented for a Self-Mixing (SM) interferometric laser vibration sensor which has been coupled with a solid state accelerometer (SSA). Previously, this was achieved by using a pre-calibration of phase and gain mismatches over the complete bandwidth of the instrument. Such a precalibration is not only tedious to execute but also hinders a mass production of the instrument as every SSA-SM sensor couple requires customized calibration. On the other hand, the proposed method does not require any pre-calibration as it uses an adaptive filter that self-tunes to match any unknown phase and gain differences between the SSA and the SM sensor. Two different adaptive algorithms, namely Recursive Least Squares (RLS) and Least Mean Squares (LMS) algorithms are tested and a comparison is established on the basis of parameter dependence, convergence time, computational cost and rms error. The proposed algorithms have provided improved results (mean error of 19.1nm and 20.2nm for LMS and RLS respectively) as compared to pre-calibration based results (mean error of 24.7 nm) for a laser wavelength of 785 nm. Simulated and experimental results thus demonstrate the utility of such an approach for embedded vibration sensing corrupted by extraneous parasitic motion.

Comparative Study on Noise Reduction Effect of Fiber Optic Hydrophone Based on LMS and NLMS Algorithm

“…Generally, L is not less than the number of non-zero eigenvalues of the autocorrelation matrix of the input signal. The range of µ is 0 < µ < 1 LS max , where S max is the maximum value of the power spectral density of the input signal [14]. If the value of µ is too small, the convergence speed will be slowed down.…”

Hardware implementation and comparison of displacement retrieval algorithms for a laser diode-based optical feedback interferometric sensor

“…The characteristics or shape of the resultant SM signal rely on the laser linewidth enhancement factor α (also known as Henry's factor [12]) as well as on the intensity of optical feedback coupling factor C (also designated as Acket's parameter [13]). A simple SM sensor of λ /2 precision can be developed by counting the transition or fringes contained by the SM signal [8] [5][6][7]. To get precision better than λ /2, the shape of fringes contained within the SM signal are studied to extract more information.…”

“…To get precision better than λ /2, the shape of fringes contained within the SM signal are studied to extract more information. The SM fringe shape is a good indicator of actual target movement [2,4,5]. By exploiting this idea, various displacement measurement SM techniques with higher precision have been proposed.…”

Detection of Self-Mixing Interferometric Fringes of a Laser Sensor Using Matched Filter

“…Self-mixing (SM) or optical feedback interferometry technique [1] has become an attractive area for the sensing of vibration [2] and biomedical applications [3]. For high precision displacement retrieval using SM sensor, one can one of many phase unwrapping methods (PUMs) [4][5][6][7].…”