Adaptive Cancellation of Parasitic Vibrations Affecting a Self-Mixing Interferometric Laser Sensor
In this paper, an adaptive method of cancellation of parasitic vibrations is presented for a self-mixing (SM) interferometric laser vibration sensor that has been coupled with a solid-state accelerometer (SSA). Previously, this was achieved using a precalibration 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 precalibration 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 errors of 19.1 nm and 20.2 nm for LMS and RLS, respectively) compared with the precalibration-based results (mean error of 24.7 nm) for a laser wavelength of 785 nm. The simulated and experimental results thus demonstrate the utility of such an approach for embedded vibration sensing corrupted by extraneous parasitic motion.
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