Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4183
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dc.contributor.authorTarai, Sangeeta-
dc.contributor.authorSahoo, Ajit Kumar-
dc.contributor.authorMaiti, Subrata-
dc.date.accessioned2023-12-27T11:38:43Z-
dc.date.available2023-12-27T11:38:43Z-
dc.date.issued2023-12-
dc.identifier.citationIEEE Microwave, Antennas, and Propagation Conference (MAPCON), Ahmedabad, India, 10-14 December 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4183-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractGround penetrating radar (GPR) is a potential method in demining techniques due to its capacity to identify plastic and metal-cased antipersonnel landmines. However, detecting landmines with GPR is difficult because other subsurface reflectors, such as stones, or metallic debris, can interfere with the detection process. This paper analyzes and evaluates a target discrimination approach on both synthetic and measurement data. It is based on significant features collected from the time-frequency domain of 1-D GPR signals using the continuous wavelet transform (CWT) and the Wigner Ville distribution (WVD). These extracted features are then fed into the Neural Network (NN) classifier for effective classification. The proposed algorithm is evaluated using radar data gathered in controlled laboratory settings utilizing stepped frequency continuous wave (SFCW) GPR. The outcomes and performance metrics of this experimentation showcase the efficacy and potential of the developed approach in landmine detection and discriminationen_US
dc.subjectGPRen_US
dc.subjecttime-frequency transformationen_US
dc.subjectWTen_US
dc.subjectWVDen_US
dc.subjectSVDen_US
dc.subjectNeural Networken_US
dc.titleClassification of Targets Using Time-Frequency Analysis of GPR Dataen_US
dc.typeArticleen_US
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