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.
This paper concerns automatic video surveillance of outdoor scenes using a single camera. The first step in automatic interpretation of the video stream is activity detection based on background subtraction. Usually, this process will generate a large number of false alarms in outdoor scenes due to e.g. movement of thicket and changes in illumination. To reduce the number of false alarms a Track Before Detect (TBD) approach is suggested. In this TBD implementation all objects detected in the background subtraction process are followed over a number of frames. An alarm is given only if a detected object shows a pattern of movement consistent with predefined rules. The method is tested on a number of video sequences and a substantial reduction in the number of false alarms is demonstrated.
When surveying an area for sea mines with a sidescan sonar, the ability to find the same object in two different sonar images is helpful to determine the nature of the object. The main problem with matching two sidescan sonar images is that a scene changes appearance when viewed from different viewpoints. This paper presents a novel approach for matching two sidescan sonar images. The method first registers the two images to ground, then uses the cross correlation of the object positions on the seabed to find the correct displacement between the two images. In order to correct any minor displacements of the relative objects position as a result of the ground registration, the object position is given an area of influence. The method is compared to an existing method for matching sidescan sonar images based on hypothetical reasoning. The two methods are compared on a number of real sidescan sonar images in which the displacement is already known, as well as on images taken of a scene from two different viewpoints. We conclude that the proposed method has fewer variables to tune in order to get satisfactory results, and that it gives better or equal results compared to the hypothetical reasoning method.
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.
The thermal properties and shape of a buried land mine can, by natural means such as diurnal cycles, result in a temperature profile on the ground surface. By exploiting the presence of this thermal signature, IR imaging has demonstrated the ability to detect buried mine-like objects. Of importance to the practical success of this technology is the ability to obtain a spatial resolution which allows discrimination of mine signatures from background clutter. This paper describes findings from a study conducted to establish the clutter statistics of natural occurring backgrounds. A novel approach is presented: the use of 2D autoregressive models to detect the unnatural variations in the background caused by buried miens. With this knowledge we have developed a process to estimate the camera resolution necessary to reliably detect and discriminate a thermal signature originating from a buried mine-like object in various terrain types.
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.
In this paper, five methods for enhancing sonar images prior to automatic detection of sea mines are investigated. Two of the methods have previously been published in connection with detection system and serve as reference. The three new enhancement approaches are a variance stabilizing log transform, nonlinear filtering, and pixel averaging for speckle reduction. The effect of the enhancement step is tested by using the full processing chain i.e. enhancement, detection and thresholding to determine the number of detections and false alarms. Substituting different enhancement algorithms in the processing chain gives a precise measure of the performance of the enhancement stage. The test is performed using a sonar image database with images ranging from very simple to very complex. The result of the comparison indicates that the new enhancement approaches improve the detection performance.
A monostatic amplitude and phase stepped-frequency radar approach have been proposed to detect small non-metallic buried anti-personnel (AP) mines. An M-56 AP-mine with a diameter of 54 mm and height of 40 mm, only, has been successfully detected and located in addition to small metallic mine-shaped objects. 2D probe-correction and addition signal processing are applied to the raw probe- data. The probe used in this experiment was an open-ended waveguide operating at S-band. The movements of the probe are controlled by two stepmotors via an RS-232 interface. The probe is connected to an HP8753C Network Analyzer through a 5 m long Sucoflex coaxial cable. The data are collected automatically using an HPIB interface. The collected data contains both the amplitude and phase information of the reflection coefficient.Data are measured at up to a maximum of 401 different frequencies at each measurement point using a mesh-grid with a resolution down to 1 mm by 1 mm. The size of the scan area is 1410 mm by 210 mm. Measurements have been performed on loamy soil containing a buried M-56, a non-metallic AP-mine, and various other mine-like objects made of solid plastic, brass, aluminum, steel, and wood. The presented results are based on probe-data measured at 100 different frequencies at each measurement point and a coarser mesh-grid of 10 mm by 10 mm, since it is found that less probe-data is needed. Our experiments show that even less amount of probe-data may be necessary.
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