Ground Penetrating Synthetic Aperture Radar Research Articles

An Intelligent Mobile Search System

This article is devoted to the development of an intelligent mobile system used for humanitarian demining. At the same time, the problems of detection, localization and storage of the obtained data are solved. The system operation is based on the use of a synthetic aperture ground penetrating radar, which makes it possible to detect mines both on the earth's surface and underground. A quadcopter is used as a carrier. A set of technical means has been developed. The central and graphic processors are used as a processing unit. Intelligent elements for processing the obtained data are convolutional neural networks, for machine learning of which a synthetic dataset was used. The data is organized into S3 segments based on various parameters, such as date, location and sensor type. This organization facilitates data retrieval and management. Data is encrypted both during transmission and at rest using AWS Key Management Service to ensure confidentiality.

Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground-Penetrating Synthetic Aperture Radar

Ground-penetrating radar mounted on a micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular GPSAR trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and an inertial measurement unit (IMU) to obtain precise, high-rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8 µs and as precise as 0.1 µs with localization rates of 1 kHz. The dual position factor formulation improves online localization accuracy up to 40 % and batch localization accuracy up to 59 % compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.

UAV-Borne FMCW InSAR for Focusing Buried Objects

Antipersonnel mines are hidden weapons that are usually buried close to the surface and are triggered by a foot step of the victim. A sensor that is able to detect minimum metal mines is a ground penetrating synthetic aperture radar (GPSAR). In previous work, an unmanned aerial vehicle (UAV)-based GPSAR was developed and tested for the detection of buried landmines. The real topography was neglected and the interface between air and soil was assumed as a horizontal plane surface. Since the dielectric properties of the soil reduce the propagation velocity of the electromagnetic wave, the interface between air and soil has to be known precisely for subsurface focusing. Neglecting the surface profile has a negative impact on the image quality and prevents the detection of buried objects. In this letter, a novel two-step procedure is used to overcome this issue. In time-division multiplex mode, the radar transmits two frequency-modulated ramps in two different frequency bands. The data of the upper frequency band are used to generate a digital elevation model (DEM) through interferometry. The data of the lower frequency band are used for GPSAR focusing on the basis of the DEM. It is demonstrated that the GPSAR image quality is improved on nonideal planar surfaces and the probability of detection is increased.

Multiview Synthetic Aperture Ground-Penetrating Radar Detection in Rough Terrain Environment: A Real-Time 3-D Forward Model

A major problem with forward-looking ground-penetrating radar (FLGPR) detection of buried explosive threats is the scattering from the rough ground surface. The authors have previously developed a real-time 3-D algorithm for single-view emulation of synthetic aperture FLGPR scattering from rough terrain at low grazing angles. This article extends the method to the real-time 3-D simulation of a multiview moving platform transmitter/receiver array moving as fast as 15 km/h. The need to perform the new calculation at every frame for the moving antenna array platform is shown to be unnecessary, with the requirement relaxed to once for only a single frame at the center of 10–20 m forward interrogation range, leading to about 50 times faster computation. The computation of the scattered waves comprising surface clutter is reduced for all moving frames to a mere multiplication of three matrices: a pre-computed impulse response matrix of rough terrain, a pre-computed correction matrix of moving frames, and a matrix characterizing the transmitting input signal. The method is evaluated via 3-D Monte Carlo simulation for various rough surface parameters, and its applicability for subsurface scattering reconstruction to characterize the buried threat objects is shown. For a vehicle-mounted FLGPR detection system, this leads to a significant saving of computation resources. Our developed algorithm provides 3-D modeling of rough terrain scattering for lossy and frequency-dispersive soil and compares well with full-wave finite-difference frequency-domain (FDFD) method computation.

Multilayer simulations for accurate geological interpretations of SHARAD radargrams

SHARAD (SHAllow RADar) is a nadir-looking Synthetic Aperture Ground Penetrating Radar on board NASA's 2005 Mars Reconnaissance Orbiter. There are three main characteristics that define the performance of this instrument: ground penetration (due to the operational frequency, the observed echoes can be related to reflections from surface or subsurface), spaceborne operation (the first reflection does not necessarily correspond to the nadir reflection), and nadir looking SAR (there will always be left/right ambiguities). All this implies that there will be surface/subsurface range ambiguity and the geological interpretation of the radargrams cannot be straightforward. In order to avoid data misinterpretation, a simulator of SHARAD’s expected response for a given observation geometry and topography is needed. Simulations can take into account all surface/subsurface reflections in order to identify common families of ambiguities and facilitate the interpretation. In this work we present SHARSIM (SHARAD Radargram SIMulator), a software tool designed to simulate SHARAD radargrams taking as inputs Mars surface information and hypothetical subsurface structure. Its performance is analyzed by investigating typical artifacts and by a direct comparison with real radargrams. We show that SHARSIM simulations can help to discern between artifacts and real subsurface features in order to make accurate geological interpretations.

A novel SVM for Ground Penetrating Synthetic Aperture Radar landmine detection

The use of vehicle-or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detection is a major challenge in practical applications of GPSAR. Support Vector Machine (SVM), employing structural risk minimization theory, does not need large amounts of training data, which makes it suitable for solving the landmine detection problem. In this paper, a novel SVM with a hypersphere instead of a hyperplane classification boundary is proposed for landmine detection in GPSAR. The HyperSphere-SVM (HS-SVM) can be trained with both landmine and clutter data, or with landmine data only, which are called the two-class HS-SVM and the one-class HS-SVM, respectively. The HS-SVM has better generalization capability than the traditional HyperPlane-SVM (HP-SVM) with respect to varying operating conditions. Quantitative comparisons have been made using real data collected with the rail-GPSAR landmine detection system, which show that both the two-class and the one-class HS-SVMs have better detection performance than the HP-SVM.

Trained detection of buried mines in SAR images via the deflection-optimal criterion

In this paper, we apply a deflection-optimal linear-quadratic detector to the detection of buried mines in images formed by a forward-looking ground-penetrating synthetic aperture radar. The detector is a linear-quadratic form that maximizes the output SNR (deflection), and its parameters are estimated from a set of training data. We show that this detector is useful when the signal to be detected is expected to be stochastic, with an unknown distribution, and when only a small set of training data is available to estimate its statistics. The detector structure can be understood in terms of the singular value decomposition; the statistical variations of the target signature are modeled using a compact set of orthogonal "eigenmodes" (or principal components) of the training dataset. Because only the largest eigenvalues and associated eigenvectors contribute, statistical variations that are underrepresented in the training data do not significantly corrupt the detector performance. The resulting detection algorithm is tested on data that are not in the training set, which has been collected at government test sites, and the algorithm performance is reported.

A Volume Scattering Model for Coupled Interpretation of Ground-Penetrating Radar (GPR) and Synthetic Aperture Radar (SAR)

We study the capabilities of low frequency radar systems to sound the subsurface in arid countries. This approach is based on the coupling between two complementary radar techniques: the airborne/spaceborne Synthetic Aperture Radar (SAR) used in L-band (1.2 GHz) for imaging large scale subsurface structures, and the Ground-Penetrating Radar (GPR) used at 900 MHz for sounding soils from the surface down to several meters. Recent results obtained on the well-known site in southern Egypt (Bir Safsaf) are recalled and constrain a numerical model for quantitatively interpreting the SAR/GPR data. This model is based on electric field extrapolation in the frequency domain, taking into account the aperture and the incidence angle of the antenna for both the SAR and GPR systems. Simulated signals from given realistic geological cross sections can then be analysed in order to understand the contribution of volume backscattering on both of radar systems. The model is tested on the Bir Safsaf site, and gives a good description of the dielectric behaviour, since observed and simulated backscattered signals are quite comparable. Discussion on the domain of validity for the model is presented as well as the perspectives that it could offer in the future missions of quantitative mapping of the subsurface in arid deserts.

Laser Doppler vibrometer-based acoustic/seismic technique and GPSAR technique for co-located landmine detection

Recent success in using a laser Doppler vibrometer (LDV) based acoustic-to-seismic landmine detection [J. M. Sabatier and N. Xiang, IEEE Trans. Geosci. Remote Sensing 39, 1146–1154 (2001); N. Xiang and J. M. Sabatier, J. Acoust. Soc. Am. 113, 1333–1341 (2003)] and a ground penetrating synthetic aperture radar (GPSAR) suggested a novel configuration of fused sensors comprised of a LDV-based A/S detection sensor and a GPSAR. Extensive field experiments revealed that these two technologies can be considered orthogonal. When used in concert, a fused configuration may significantly improve the probability of detection and reduce the false alarm rate. They function best against different types of landmines under different burial conditions because they exploit disparate phenomena to detect mines. In order to optimize the fused detection ability using the two modalities, co-located field experiments have been conducted using both LDV-based A/S sensor and a GPSAR sensor. This paper will discuss the comparative experimental study using the recent co-located field scanning results.

Propagation velocity uncertainty on GPR SAR processing

To detect buried landmines, Planning Systems Incorporated (PSI) has developed a ground-penetrating synthetic aperture radar (GPSAR) system. Since the electromagnetic wave propagation velocity in the soil depends on many factors, velocity uncertainty is inevitable. However, we have observed that, unlike conventional airborne/spaceborne synthetic aperture radar (SAR) systems, the PSI GPSAR system is very robust against the velocity uncertainty under mild conditions. Theoretical analysis is provided to explain this observation. Although our discussion is based on the PSI GPSAR system, it applies to other GPR-based (ground-penetrating radar) landmine detection systems as well.