Using neural networks for high resolution distance measurements in pulsed laser radar

We have developed a new distance measurement method which can obtain distance information directly from the output waveform of pulsed laser radar (PLR). A simple digital signal processing technique and multilayer perceptrons (MLP) have been used to recognize the pulse shape and to obtain the distance information. 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 decreasing the noise and preprocessing of the noisy input pulse shapes to the neural network, resolution and nonlinearity were greatly improved. Distance deviation of 53 /spl mu/m-168 /spl mu/m, full width at half power (FWHP) of 70 /spl mu/m-190 /spl mu/m and non-linearity of 187 /spl mu/m have been achieved. All the measurements in the same situation has been performed by using the standard method to extract the distance information from time interval between the reference pulse and the reflected pulse. In comparison with the standard method, resolution in the best case and non-linearity 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. Because the neural network decreases 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.