Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4183
Title: Classification of Targets Using Time-Frequency Analysis of GPR Data
Authors: Tarai, Sangeeta
Sahoo, Ajit Kumar
Maiti, Subrata
Keywords: GPR
time-frequency transformation
WT
WVD
SVD
Neural Network
Issue Date: Dec-2023
Citation: IEEE Microwave, Antennas, and Propagation Conference (MAPCON), Ahmedabad, India, 10-14 December 2023
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
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/4183
Appears in Collections:Conference Papers

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