Signal processing and pattern recognition methods for radar AP mine detection and identification

Kempen, Luc Van; Sahli, Hanene; Nyssen, Edgard; Cornelis, Jan

doi:10.1049/cp:19980694

Cited by 14 publications

(6 citation statements)

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“…Several methods for clutter reduction and detection in GPR and EMIS data exist. The literature suggests a number of methods, such as likelihood ratio testing, 1 parametric system identification and recognition, [2][3][4][5] wavelet packet decomposition, 6, 7 subspace techniques, 8 and simple mean subtraction.…”

Section: Introductionmentioning

confidence: 99%

A comparative and combined study of EMIS and GPR detectors by the use of independent component analysis

et al. 2005

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Independent Component Analysis (ICA) is applied to classify unexploded ordnance (UXO) on laboratory UXO test-field data, acquired by stand-off detection. The data are acquired by an Electromagnetic Induction Spectroscopy (EMIS) metal detector and a ground penetrating radar (GPR) detector. The metal detector is a GEM-3, which is a monostatic sensor measuring the response of the environment on a multi-frequency constant wave excitation field (300 Hz to 25 kHz), and the GPR detector is a stepped-frequency GPR with a monostatic bow-tie antenna (500 MHz to 2.5 GHz). For both sensors the in-phase and the quadrature responses are measured at each frequency. The test field is a box of soil where a wide range of UXOs are placed at selected positions. The position and movement of both of the detectors are controlled by a 2D-scanner. Thus the data are acquired at well-defined measurement points. The data are processed by the use of statistical signal processing based on ICA. An unsupervised method based on ICA to detect, discriminate, and classify the UXOs from clutter is suggested. The approach is studied on GPR and EMIS data, separately and compared. The potential is an improved ability: to detect the UXOs, to evaluate the related characteristics, and to reduce the number of false alarms from harmless objects and clutter.

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Section: Introductionmentioning

confidence: 99%

A comparative and combined study of EMIS and GPR detectors by the use of independent component analysis

et al. 2005

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“…A basic time-frequency representation is the Wigner distribution (WD). Despite the WD has been used for GPR signal analysis and particularly for target recognition by many researchers, [8][9][10][11][12] we think that its discriminative potential has not been yet explored fully. In the meantime the rapid development of the wavelet theory with applications has led to an alternate signal representation, namely time-scale representation.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of UWB deconvolution and feature-extraction algorithms for GPR landmine detection

Savelyev

Suzuki

2004

Detection and Remediation Technologies for Mines and Minelike Targets IX

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In this work we developed target recognition algorithms for landmine detection with ultra-wideband ground penetrating radar (UWB GPR). Due to non-stationarity of UWB signals their processing requires advanced techniques, namely regularized deconvolution, time-frequency or time-scale analysis. We use deconvolution to remove GPR and soil characteristics from the received signals. An efficient algorithm of deconvolution, based on a regularized Wiener inverse filter with wavelet noise level estimation, has been developed. Criteria of efficiency were stability of the signal after deconvolution, difference between the received signal and the convolved back signal, and computational speed. The novelty of the algorithm is noise level estimation with wavelet decomposition, which defines the noise level separately for any signal, independently of its statistics. The algorithm was compared with an iterative time-domain deconvolution algorithm based on regularization. For target recognition we apply singular value decomposition (SVD) to a time-frequency signal distribution. Here we compare the Wigner transform and continuous wavelet transform (CWT) for discriminant feature selection. The developed algorithms have been checked on the data acquired with a stepped-frequency GPR.

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“…The literature suggests a number of clutter reduction and detection methods, such as likelihood ratio testing 4 , parametric system identification [5][6][7][8] , wavelet packet decomposition 9, 10 , subspace techniques [11][12][13][14][15] , and simple mean subtraction 2 . However, many of these fail to detect shallow buried AP-landmines, mostly because of the statistical nature of the clutter, e.g., the ground surface is not perfectly flat nor even relative smooth.…”

Section: Introductionmentioning

confidence: 99%

GPR detection of buried symmetrically shaped minelike objects using selective independent component analysis

et al. 2003

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This paper addresses the detection of mine-like objects in stepped-frequency ground penetrating radar (SF-GPR) data as a function of object size, object content, and burial depth. The detection approach is based on a Selective Independent Component Analysis (SICA). SICA provides an automatic ranking of components, which enables the suppression of clutter, hence extraction of components carrying mine information. The goal of the investigation is to evaluate various time and frequency domain ICA approaches based on SICA. The performance comparison is based on a series of mine-like objects ranging from small-scale anti-personal (AP) mines to largescale anti-tank (AT) mines. Large-scale SF-GPR measurements on this series of mine-like objects buried in soil were performed. The SF-GPR data was acquired using a wideband monostatic bow-tie antenna operating in the frequency range 750 MHz − 3.0 GHz. The detection and clutter reduction approaches based on SICA are successfully evaluated on this SF-GPR dataset.

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Signal processing and pattern recognition methods for radar AP mine detection and identification

Cited by 14 publications

References 0 publications

A comparative and combined study of EMIS and GPR detectors by the use of independent component analysis

A comparative and combined study of EMIS and GPR detectors by the use of independent component analysis

Comparative analysis of UWB deconvolution and feature-extraction algorithms for GPR landmine detection

GPR detection of buried symmetrically shaped minelike objects using selective independent component analysis

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