Abstract / truncated to 115 words (read the full abstract)

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 ... toggle 14 keywords

sonar sensing – target differentiation – target localization – artificial neural networks – learning – feature extraction – statistical pattern recognition – dempster-shafer evidential reasoning – majority voting – sensing systems – acoustic signal processing – mobile robots – map building – voronoi diagram.

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