KEYWORDS: Sensors, Independent component analysis, General packet radio service, Electromagnetic coupling, Land mines, Data modeling, Principal component analysis, Data acquisition, Target detection, Metals
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 25 kHz), and the GPR detector is a stepped-frequency GPR with a monostatic bow-tie antenna (500 MHz 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, both separately and combined. 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.
KEYWORDS: Independent component analysis, General packet radio service, Signal detection, Land mines, Data modeling, Principal component analysis, Antennas, Mining, Sensors, Signal processing
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 large-scale 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.
KEYWORDS: Independent component analysis, Land mines, General packet radio service, Antennas, Principal component analysis, Iron, Ground penetrating radar, Signal detection, Data acquisition, Feature selection
Statistical signal processing approaches based on Independent Component Analysis (ICA) algorithms for clutter reduction in Stepped-Frequency Ground Penetrating Radar (SF-GPR) data are presented. The purpose of the clutter reduction is indirectly to decompose the GPR data into clutter reduced GPR data and clutter. The experiments indicate that ICA algorithms can decompose GPR data into suitable subspace components, which makes it possible to select a subset of components containing primarily target information (like anti-personal landmines) and others which contain mainly clutter information. The paper compares spatial and temporal ICA approaches on field-test data from shallow buried iron and plastic anti-personal landmines. The data are acquired using a monostatic bow-tie antenna operating in the frequency range from 500 MHz to 2.5 GHz.
KEYWORDS: Principal component analysis, Mining, Resolution enhancement technologies, Land mines, Reflection, Iron, Signal processing, Optical signal processing, General packet radio service, Ground penetrating radar
Proper clutter reduction is essential for Ground Penetrating Radar data since low signal-to-clutter ratio prevent correct detection of mine objects. A signal processing approach for resolution enhancement and clutter reduction used on Stepped-Frequency Ground Penetrating Radar (SF-GPR) data is presented, and the effects of combining clutter reduction with resolution enhancement are examined using simulated SF-GPR data examples. The resolution enhancement method is based on methods from optical signal processing and is largely carried out in the frequency domain to reduce the computational burden. The clutter reduction method is based on basis function decomposition of the SF-GPR time-series from which the clutter and the signal are separated.
KEYWORDS: Antennas, Mining, Land mines, Principal component analysis, Interfaces, Metals, Electroluminescence, General packet radio service, Ground penetrating radar, Waveguides
The result form field-tests using a Stepped-Frequency Ground Penetrating Radar (SF-GPR) and promising antenna and air- ground deembedding methods for a SF-GPR is presented. A monostatic S-band rectangular waveguide antenna was used in the field-tests. The advantages of the SF-GPR, e.g., amplitude and phase information in the SF-GPR signal, is used to deembed the characteristics of the antenna. We propose a new air-to-ground interface deembedding technique based on Principal Component Analysis which enables enhancement of the SF-GPR signal from buried objects, e.g., anti-personal landmines. The methods are successfully evaluate on field-test data obtained from measurements on a large-scale in-door test field.
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