Ultrasonic multi-transducer processing for pattern recognition

This paper discusses the development of a new binaural ultrasonic sensor for mobile robot localisation and differentiation of simple objects, without environment scanning. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, pattern representation, environment sensing, feature extraction and selection, classifier design and teaming. The recognition of objects (plane, corner, edge and cylinder) is achieved by processing of sonar signal using statistical methods (K nearest neighbours, linear and quadratic discriminant analysis), the parzen window method and neural networks which first identify and then exploit echo features: the frequency, slope, surface, length, amplitude and time-of-flight (TOF) defined as characteristics of these objects. In our study, several methods are used to extract the most discriminant features set, like sequential methods (Backward and Forward), optimal method (branch and bound). In addition, we use the principal component analysis (PCA) method to provide the correlation between the discriminant parameters.