Real-Time Adaptive Spectral Filter Based Parasitic Vibration Cancellation in Self-Mixing Laser Sensor Signals

Abstract

Parasitic motion corrupts the measurement of all non-contact sensor systems. It thus hinders deployment of these sensors for such real-world applications where it is impossible to keep the sensor perfectly stationary. In this paper, an adaptive algorithm is presented to remove parasitic vibrations corrupting the measurements of a self-mixing (SM) interferometric laser sensor. Previously, this was achieved by coupling a solid state accelerometer (SSA) with the SM sensor followed by either a pre-calibration based design or a time-domain adaptive filter based system. The proposed method is based on adaptive spectral filtering where filter coefficients are derived based on the parasitic vibrations present in the retrieved target motion. Importantly, it neither requires any pre-calibration procedure nor has any dependence on parameters such as filter-order, convergence criteria, or step-size (required in case of time-domain adaptive filters), thus making the proposed scheme a suitable choice for mass production of SSA-SM sensor systems. The proposed algorithm provides improved mean RMS error of 5.37 nm as opposed to 19.1 nm for least mean square (LMS) filter, 20.22 nm for recursive least square (RLS) filter, and 24.7 nm for pre-calibration based system. This method’s performance has also been characterized and an embedded, real-time, hardware design is also presented. At the end, hardware system results (on experimental data-set) and sensor bounds/bandwidth are also quantified.