Comparative analysis of different approaches to target classification and localization with sonar
dc.citation.epage | 30 | en_US |
dc.citation.spage | 25 | en_US |
dc.contributor.author | Ayrulu, Birsel | en_US |
dc.contributor.author | Barshan, Billur | en_US |
dc.coverage.spatial | Baden-Baden, Germany, | |
dc.date.accessioned | 2016-02-08T11:57:19Z | |
dc.date.available | 2016-02-08T11:57:19Z | |
dc.date.issued | 2001-08 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 20-22 Aug. 2001 | |
dc.description | Conference name: Conference Documentation International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI 2001 | |
dc.description.abstract | The comparison of different classification and fusion techniques was done for target classification and localization with sonar. Target localization performance of artificial neural networks (ANN) was found to be better than the target differentiation algorithm (TDA) and fusion techniques. The target classification performance of non-parametric approaches was better than that of parameterized density estimator (PDE) using homoscedastic and heteroscedastic NM for statistical pattern recognition techniques. | en_US |
dc.description.provenance | Made available in DSpace on 2016-02-08T11:57:19Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2001 | en |
dc.identifier.doi | 10.1109/MFI.2001.1013503 | |
dc.identifier.uri | http://hdl.handle.net/11693/27592 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | |
dc.relation.isversionof | https://doi.org/10.1109/MFI.2001.1013503 | |
dc.source.title | Conference Documentation International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI 2001 | en_US |
dc.subject | Artificial neural networks | en_US |
dc.subject | Evidential reasoning | en_US |
dc.subject | Localization | en_US |
dc.subject | Sensor data fusion | en_US |
dc.subject | Sonar | en_US |
dc.subject | Statistical pattern recognition | en_US |
dc.subject | Target classification | en_US |
dc.subject | Voting | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Classification (of information) | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Pattern recognition | en_US |
dc.subject | Ultrasonic transducers | en_US |
dc.subject | Intelligent systems | en_US |
dc.subject | Sonar | en_US |
dc.title | Comparative analysis of different approaches to target classification and localization with sonar | en_US |
dc.type | Conference Paper | en_US |
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