Barshan, B.Ayrulu, B.2016-02-082016-02-0820030031-3203http://hdl.handle.net/11693/24495This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. 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. Different representations of amplitude and time-of-2ight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained usingpre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.EnglishArtificial neural networksDempster-Shafer evidential reasoningFuzzy c-means clusteringKernel estimatorLinear discriminant analysisMajority votingNearest-neighbor classifierParameterized density estimationSonar sensingTarget classificationTarget differentiationTarget localizationAcoustic signal processingCollision avoidanceFourier transformsKnowledge based systemsNavigationNeural networksSonarWavelet transformsTarget trackingPattern recognitionArticle10.1016/S0031-3203(02)00167-X