Abstract:
Parasitic motion corrupts the measurement of all non-contact sensor systems.
It thus hinders deployment of such sensors for such applications where it is
impossible to keep the sensor perfectly stationary. In this work, 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 parame ters such as filter-order, convergence criteria, or step size (required in case of
adaptive filters), thus making the proposed scheme a suitable choice for mass
production of SSA-SM sensor systems. The proposed algorithm provides im proved mean RMS error of 5.37 nm as opposed to 19.1 nm least mean square
(LMS), 20.22 nm recursive least square (RLS) and 24.7 nm (pre-calibration
based system). This method’s performance also been characterized and an
embedded, real-time, sensor design is also presented. At the end, hardware
system results (on experimental data-set) and sensor bounds/bandwidth are
also quantified.
