Neural Method for Two Dimensional (2D) High Contrast Imaging in Pulsed Laser Radar

Abstract

In this paper we have developed a new imaging method which can obtain the grey levels directly from the output waveform of Pulsed Laser Radar (PLR). A simple digital signal processing technique and multi layer perceptrons (MLP) type neural network (NN) have been used to obtain the grey level information from the pulse shapes. The method has been implemented in a real PLR to improve contrast and speed of 2D imaging in PLR. For comparison with the standard method, a picture consisting of 16 grey levels (from 0 for black to 1 for white) using both methods. Because of the ability of NNs in extracting the information from nonlinear and noisy data and pre-processing of the noisy input pulse shapes to the NN, the average and maximum errors in the grey levels in comparison with standard method more than 88.5% and 72.6% improved, respectively. Because in this method the effect of the noise is decreased, it is possible to make to image at the same resolution as in standard method with lower averaging in the sampling unit and this dramatically increases speed of the measurements.