Application of neural networks for extraction of distance and reflectance in pulsed laser radar

Abstract

In this paper a new method has been developed whereby distance and reflectance information can be obtained directly from the output waveform of pulsed laser radar (PLR). A simple digital signal processing technique and multilayer perceptrons (MLP) type neural networks have been used to recognize the pulse shapes and to obtain the information. The method has been applied to PLR for three purposes: high-resolution distance measurement, very short distance measurement and two-dimensional (2D) imaging. In order to present a precise evaluation, all measurements were performed simultaneously by the new approach and the standard method of pulsed time of flight, which is very often used for extraction of information from the output signal of PLR system. In comparison with the standard method, in the high-resolution distance measurement applications, the non-linearity errors are lowered by more than 6% and the standard deviation of the single-shot distance measurement results is lowered by almost one order. In very short distance measurement applications, for the first time distance measurements with overlapping of the reference and reflected pulses have been possible with no loss in distance resolution. In the reflectance measurement applications (2D imaging), the average error and maximum error in the reflectance measurements have been improved by 88.5% and 72.6%, respectively. In all cases, in addition to these improvements, since the new method decreases the effect of the noise, it is possible to make the measurements with the same resolution as the standard method, but with a lower averaging in the sampling unit thus drastically increasing the speed of the measurements.