In this paper we have developed a new distance measurement method which can obtain distance information directly from the output waveform of PLR. A simple digital signal processing technique and multi layer perceptrons (MLP) type neural network (NN) have been used to recognise the pulse shapes and to obtain the distance information. In the first part, the method has been implemented in a real PLR for high resolution distance measurements to improve the resolution and to decrease the nonlinearity error. Because of the ability of neural networks in extracting the information from the noisy data and pre-processing of the noisy input pulse shapes to the neural network, resolution and non-linearity were greatly improved. Distance deviation of 53 μm-168 gm, full width at half power (FWHP) of 70 um-190 um and nonlinearity error of 187 gm have been achieved. In comparison with the standard method, resolution in the best case and nonlinearity error were improved by 86% and 6.5% respectively. In this method if the PLR system is reasonably stable during the measurement, it is possible to use only the reflected pulse from the target to extract the distance information and this makes PLR simpler in hardware. In the second part, the method has been implemented to make possible very short distance measurements with overlapping of reference and reflected pulses. In this part, a distance standard deviation of 300 gm and an average nonlinearity error of 500 pm are achieved. In both parts, because the neural method decreases the effect of the noise, it is possible to make the measurements with the same resolution of standard method but with the lower averaging in sampling unit and this dramatically increase the speed of the measurement.

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