Neural network based target differentiation using sonar for robotics applications

Abstract

This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor environments. The neural network can differentiate more targets, and achieves high differentiation and localization accuracy, improving on previously reported methods. It achieves this by exploiting the identifying features in the differential amplitude and time-of-flight characteristics of these targets. An important observation follows from the robustness tests, which indicate that the amplitude information is more crucial than time-of-flight for reliable operation. The study suggests wider use of neural networks and amplitude information in sonar-based mobile robotics.