Learning Signal Processing System Using a Neural Network Model for Laser Doppler Velocimetry

Abstract

This study proposed a new signal processing system using a neural network model for the laser Doppler velocimetry (LDV). The proposed system was based on spectrum analysis using FFT and its basic structure with the burst spectrum analyzer (BSA). Though the BSA is one of useful signal processors of the LDV, accuracy of the velocity measurements decreases at low S/N ratio. So a neural network model was constructed in the BSA system in order to increase the accuracy of measurement. The model learns variance of the Doppler frequency related to velocity to predict the succeeding Doppler frequency. Thereafter, the post processor calculates a weight function in the spectral domain and computers the Doppler frequency from the weighted spectrum.
Actual velocity measurement on a rotating acrylic disk was carried out and the results showed high availability of the system. It allowed increase in the accuracy of velocity measurement.