Title :
Detection and classification of landmines using AR modeling of GPR data
Author :
Deiana, Daniela ; Anitori, Laura
Author_Institution :
TNO Defence, Security & Safety, The Hague, Netherlands
Abstract :
In this paper we present some results on detection and classification of low metal content anti personnel (AP) landmines using a modified version of the Auto Regressive (AR) modeling algorithm presented in. A statistical distance is computed between the AR coefficients of the measured GPR time signal and the AR coefficients of a reference database (containing the AR models of the mines of interest) and a detection is declared if this distance is below a given threshold.
Keywords :
autoregressive processes; ground penetrating radar; landmine detection; radar detection; AR coefficients; GPR data AR modeling; GPR time signal; autoregressive modeling algorithm; landmine classification; low metal content antipersonnel landmine detection; reference database; Aluminum; Databases; Electromagnetic measurements; Electromagnetic modeling; Finite difference methods; Ground penetrating radar; Landmine detection; Radar antennas; Soil; Time domain analysis; AR Modeling; GPR; Landmines;
Conference_Titel :
Ground Penetrating Radar (GPR), 2010 13th International Conference on
Conference_Location :
Lecce
Print_ISBN :
978-1-4244-4604-9
Electronic_ISBN :
978-1-4244-4605-6
DOI :
10.1109/ICGPR.2010.5550141
