The problem of detecting buried objects has engaged radar system developers for quite some time. Many
systems—both experimental and commercial—have been developed, including vehicle-mounted systems
that look beneath road surfaces. Most of these downward-looking systems exploit multiple transmit and
receive channels to enhance resolution in the final radar imagery used for target detection. In such a
system, the configuration and operation of the various transmit and receive elements play a critical role in
the quality of the output imagery. In what follows, we leverage high-fidelity electromagnetic model data
to examine a multistatic downward-looking radar system. We evaluate the signatures produced by various
targets of interest and describe, both qualitatively and quantitatively, the variations in target signatures
produced by different system configurations. Finally, we analyze the underlying physics of the problem to
explain certain characteristics in the observed target signatures.
KEYWORDS: Radar, Image processing algorithms and systems, Synthetic aperture radar, Image segmentation, Image processing, Image acquisition, Data processing, Signal processing, Antennas, Global Positioning System
Researchers at the U.S. Army Research Laboratory (ARL) designed and fabricated the Synchronous Impulse
REconstruction (SIRE) radar system in an effort to address fundamental questions about the utilization of low
frequency, ultrawideband (UWB) radar. The SIRE system includes a receive array comprising 16 receive channels,
and it is capable of operating in either a forward-looking or a side-looking mode. When operated in side-looking
mode, it is capable of producing high-resolution Synthetic Aperture Radar (SAR) data. The SAR imaging
algorithms, however, initially operated under the assumption that the vehicle followed a nearly linear trajectory
throughout the data collection. Under this assumption, the introduction of vehicle path nonlinearities distorted the
processed SAR imagery. In an effort to mitigate these effects, we first incorporated segmentation routines to
eliminate highly non-linear portions of the path. We then enhanced the image formation algorithm, enabling it to
process data collected from a non-linear vehicle trajectory.
We describe the incorporated segmentation approaches and compare the imagery created before and after their
incorporation. Next, we describe the modified image formation algorithm and present examples of output imagery
produced by it. Finally, we compare imagery produced by the initial segmentation algorithm to imagery produced by
the modified image-formation algorithm, highlighting the effects of segmentation parameter variation on the final
The Army Research Laboratory (ARL) has recently developed the ground-based synchronous impulse reconstruction
(SIRE) radar - a low-frequency radar capable of exploiting both a real antenna array and along-track integration
techniques to increase the quality of processed imagery. We have already demonstrated the system's utility by imaging
static scenes. In this paper we address the moving target indication (MTI) problem, and we demonstrate the impulse-based
system's ability to both detect and locate slowly moving targets. We begin by briefly describing the SIRE system
itself as well as the system configuration utilized in collecting the MTI data. Next we discuss the signal processing
techniques employed to create the final MTI image. Finally, we present processed imagery illustrating the utility of the
Change detection provides a powerful tool for detecting the introduction of weapons or hazardous materials into an area
under surveillance, as demonstrated in past work carried out at the Army Research Laboratory (ARL). This earlier work
demonstrated the potential for detecting recently emplaced surface landmines using an X-Band, synthetic aperture radar
(SAR) sensor. Recent experiments conducted at ARL have extended these change detection results to imagery collected
by the synthetic impulse reconstruction (SIRE) radar - a lower-frequency system developed at ARL. In this paper we
describe the algorithms adopted for this change detection experiment and present results obtained by applying these
algorithms to the SIRE data set. Results indicate the potential for utilizing systems such as the SIRE as surveillance
The Microwave Sensors Branch of the Army Research Laboratory (ARL) recently evaluated the potential of a commercially available borehole radar system for an underground target detection application. We used this ground-penetrating system, which is capable of operation at either 100 or 250 MHz, to conduct experiments at a locally constructed test site. Since the site's soil characteristics would severely impact conclusions drawn from the collected data, we also obtained and analyzed soil samples in order to determine the electrical properties of the earth in the vicinity of the boreholes. In addition, we modeled and then built a canonical target, using this canonical target as an input to electromagnetic simulations. The outputs from these simulations guided us in the analysis and interpretation of the collected radar data.
In this paper, we present a description of both the data collection itself and the results of a posteriori analysis of the collected data. We begin by describing the test site along with the procedures that we followed when conducting the experiments. Next, we present a soil analysis and the expected target radar cross section (RCS) obtained from the electromagnetic modeling simulations. We then discuss the implications of these results for system performance. Finally, we present an analysis of real data from the collection and compare it to what we expect based on the soil analysis and the output of the electromagnetic models. Collectively, these analyses provide an indication of the borehole radar's true potential for detecting underground targets.
Proc. SPIE. 5794, Detection and Remediation Technologies for Mines and Minelike Targets X
KEYWORDS: Target detection, Detection and tracking algorithms, Synthetic aperture radar, Image processing, Image registration, X band, Monte Carlo methods, Image quality, Palladium, Algorithm development
Multi-look processing provides a straightforward method for enhancing the quality of grainy (speckle-filled) synthetic aperture radar (SAR) imagery. This improvement in quality results from a reduction in variability of the individual pixel values brought about by non-coherent averaging of multiple, statistically independent views of the same scene. That is, the variance of the average of independent, identically distributed random variables is inversely proportional to the number of independent looks that are averaged. Of course, the quality of the averaged image depends heavily on the accuracy of the algorithm used to align the individual looks prior to averaging. The Army Research Laboratory (ARL) has recently applied multi-look processing techniques to sets of images from independent views to generate look-averaged images. In the course of this processing, we have identified the need to quantify any degradation in multi-look image quality resulting from mismatches in registration of the individual looks used to form the look-averaged image. Since these degradations can affect the performance of automatic target detection algorithms, we have also identified the need to quantify the effects of look-averaged image degradation on the performance of such algorithms. In this paper, we use X-Band SAR data to determine the extent to which translational and rotational errors in the registration of individual looks degrade the quality of the resulting multi-look averaged image. We then examine how this degradation in image quality impacts the automatic detection of small targets in the final, look-averaged image. Finally, we introduce variations in target-to-clutter ratio within each of the individual looks and analyze how these changes affect the resulting look-averaged image.
As the Army moves toward more lightly armored Future Combat System (FCS) vehicles, enemy personnel will present an increasing threat to U.S. soldiers. In particular, they face a very real threat from adversaries using shoulder-launched, rocket propelled grenade (RPG). The Army Research Laboratory has utilized its Aberdeen Proving Ground (APG) turntable facility to collect very high resolution, fully polarimetric Ka band radar data at low depression angles of a man holding an RPG. In this paper, we examine the resulting low resolution and high resolution range profiles; and based on the observed radar cross section (RCS) value, we attempt to determine the utility of Ka band radar for detecting enemy personnel carrying RPG launchers.
Detection of stationary targets with a real-aperture radar requires an algorithm that is a function of suitably defined features. The definition and values of these features are normally dependent on bandwidth, look-averaging, and polarization. Since the use of a fully polarimetric radar may not be feasible for low-cost radars on ground platforms, the goal of this effort is to investigate trade-offs between polarization and bandwidth. In this paper, we present prescreener and quadratic polynomial discriminator performance comparisons as a function of polarization, bandwidth, and look-averaging.
Many ultra-wideband (UWB) synthetic aperture radar (SAR) detection agorithms employ some combination of a set of features, calculated from the incoming raw radar data return, to segregate targets from clutter in a SAR image. Based on the training data, the algorithm designer selects those features that exploit some difference in the physical characteristics between the target class and clutter class. A detection algorithm is then trained to determine values for a set of algorithm parameters that will minimize some sort of error criterion. The physical characteristics that guide the feature selection can change, however, with changes in the attributes of the data collection, such as the depression angle from the radar to the point of interest. When the depression angle changes, the algorithm parameters that were optimal for the training data may no longer be optimal for test data at a different depression angle. We examine the changes in detector performance resulting from depression angle mismatches between the training and test data sets.
Object recognition can be parametrized systematically through physically robust wave objects by linking features (observables) in scattered field data with features on the object (target) giving rise to the data. The wave objects are broadly separated into global (mode) and local (wavefront) categories. Their parametrization requires different wave-oriented signal- processing algorithms which are implemented conveniently in relevant subdomains of the configuration (space-time) spectrum (wavenumber-frequency) phase space. Projection of scattering data onto the phase space is achieved via Gaussian-windowed Fourier transforms, wavelet transforms, and windowed model-based (superresolution) algorithms. Example results are presented here for time-domain modes excited by an open cavity as well as by periodic and quasi-periodic structures, with data processed in the time-frequency phase space. Additionally, we consider frequency-domain modes (leaky modes supported by a dielectric slab) which are processed in the space-wavenumber phase space. For some situations, it is more appropriate to process the entire database simultaneously (without windowing), and we have used such techniques for certain modal and wavefront parametrizations. Concerning modal 'footprinting', results are presented for superresolution processing of measured short-pulse scattering data from resonant targets embedded in foliage (foliage penetrating radar); in these examples we extract late-time target resonant frequencies. We have also applied superresolution algorithms to wavefront-based processing, and results are presented here for model targets.
A small minefield was deployed in the desert near Yuma, Arizona in June of 1993. Radar data of this minefield was collected by ground-based and airborne radar sensors. The minefield consists of M-20 metal and M-80 plastic anti-armor mines and Valmara-69 antipersonnel mines. The mines were deployed on the surface and buried at three different depths. Images and analysis of the minefield, which are derived from data collected by the SRI FOLPEN II synthetic aperture radar, are presented here. The minefield was imaged over three bands from 100 to 500 MHz and at various depression angles with this radar sensor. The image analysis is compared to the modeling results of surface and buried mine-like objects. We also show the results of a new radio frequency interference (RFI) rejection algorithm and the image quality improvement we achieved.