Comparative analysis of different approaches to target differentiation and localization with sonar

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dc.citation.epage1231en_US
dc.citation.issueNumber5en_US
dc.citation.spage1213en_US
dc.citation.volumeNumber36en_US
dc.contributor.authorBarshan, B.en_US
dc.contributor.authorAyrulu, B.en_US
dc.date.accessioned2016-02-08T10:30:14Z
dc.date.available2016-02-08T10:30:14Z
dc.date.issued2003en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThis 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.en_US
dc.identifier.doi10.1016/S0031-3203(02)00167-Xen_US
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/11693/24495
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/S0031-3203(02)00167-Xen_US
dc.source.titlePattern Recognitionen_US
dc.subjectArtificial neural networksen_US
dc.subjectDempster-Shafer evidential reasoningen_US
dc.subjectFuzzy c-means clusteringen_US
dc.subjectKernel estimatoren_US
dc.subjectLinear discriminant analysisen_US
dc.subjectMajority votingen_US
dc.subjectNearest-neighbor classifieren_US
dc.subjectParameterized density estimationen_US
dc.subjectSonar sensingen_US
dc.subjectTarget classificationen_US
dc.subjectTarget differentiationen_US
dc.subjectTarget localizationen_US
dc.subjectAcoustic signal processingen_US
dc.subjectCollision avoidanceen_US
dc.subjectFourier transformsen_US
dc.subjectKnowledge based systemsen_US
dc.subjectNavigationen_US
dc.subjectNeural networksen_US
dc.subjectSonaren_US
dc.subjectWavelet transformsen_US
dc.subjectTarget trackingen_US
dc.subjectPattern recognitionen_US
dc.titleComparative analysis of different approaches to target differentiation and localization with sonaren_US
dc.typeArticleen_US

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