A COMPARISON OF DIFFERENT APPROACHES TO TARGET DIFFERENTIATION WITH SONAR

This study compares the performances of different classification schemes and fusion techniques for target differentiation and localization of commonly encountered features in indoor robot environments using sonar sensing. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map-building, navigation, obstacle avoidance, and target tracking. The classification schemes employed include the target differentiation algorithm developed by Ayrulu and Barshan, statistical pattern recognition techniques, fuzzy c-means clustering algorithm, and artificial neural networks. The fusion techniques used are Dempster-Shafer evidential reasoning and different voting schemes. To solve the consistency problem arising in simple majority voting, different voting schemes including preference ordering and reliability measures are proposed and verified experimentally. To improve the performance of neural network classifiers, different input signal representations, two different training algorithms, and both modular and non-modular network structures are considered. The best classification and localization scheme is found to be the neural network classifier trained with the wavelet transform of the sonar signals. This method is applied to map-building in mobile robot environments. Physically different sensors such as infrared sensors and structured-light systems besides sonar sensors are also considered to improve the performance in target classification and localization.