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http://hdl.handle.net/2080/4183
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DC Field | Value | Language |
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dc.contributor.author | Tarai, Sangeeta | - |
dc.contributor.author | Sahoo, Ajit Kumar | - |
dc.contributor.author | Maiti, Subrata | - |
dc.date.accessioned | 2023-12-27T11:38:43Z | - |
dc.date.available | 2023-12-27T11:38:43Z | - |
dc.date.issued | 2023-12 | - |
dc.identifier.citation | IEEE Microwave, Antennas, and Propagation Conference (MAPCON), Ahmedabad, India, 10-14 December 2023 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/4183 | - |
dc.description | Copyright belongs to proceeding publisher | en_US |
dc.description.abstract | Ground 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 discrimination | en_US |
dc.subject | GPR | en_US |
dc.subject | time-frequency transformation | en_US |
dc.subject | WT | en_US |
dc.subject | WVD | en_US |
dc.subject | SVD | en_US |
dc.subject | Neural Network | en_US |
dc.title | Classification of Targets Using Time-Frequency Analysis of GPR Data | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2023_MPACON_STarai_Classification.pdf | 547.18 kB | Adobe PDF | View/Open Request a copy |
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