The U.S. Army Research Laboratory has developed the Spectrally Agile Frequency-Incrementing Reconfigurable (SAFIRE) radar, which is capable of imaging concealed/buried targets using forward- and side-looking configurations. The SAFIRE radar is vehicle-mounted and operates from 300 MHz–2 GHz; the step size can be adjusted in multiples of 1 MHz. It is also spectrally agile and capable of excising frequency bands, which makes it ideal for operation in congested and/or contested radio frequency (RF) environments. Furthermore, the SAFIRE radar receiver has a super-heterodyne architecture, which was designed so that intermodulation products caused by interfering signals could be easily filtered from the desired received signal. The SAFIRE system also includes electro-optical (EO) and infrared (IR) cameras, which can be fused with radar data and displayed in a stereoscopic augmented reality user interface. In this paper, recent upgrades to the SAFIRE system are discussed and results from the SAFIRE’s initial field tests are presented.
A new, versatile, UHF/L band, ultrawideband (UWB), vehicle-mounted radar system developed at the U.S. Army Research Laboratory (ARL) has recently been exercised at an arid U.S. test site. The unique switching scheme implemented to record data from all receive channels is described, along with the current calibration procedure. Radar and global positioning system (GPS) data collected in both forwardand side-looking configurations are processed, and synthetic aperture radar (SAR) images are formed. Results are presented for various target emplacement scenarios.
The U.S. Army Research Laboratory (ARL) has recently upgraded the indoor, rail-mounted synthetic aperture radar (SAR) system, RailSAR, to enable collection of large amounts of low-frequency, ultrawideband (UWB) data. Our intent is to provide a research tool that is capable of emulating airborne SAR configuration and associated data collection geometries against surrogate explosive hazard threat deployments. By having such a capability, ARL’s facility will afford a more rapid response to the ever changing improvised characteristics associated with explosive hazards today and in the future. Therefore, upgrades to this RailSAR tool to improve functionality and performance are needed to meet the potential rapid response assessments to be carried out. The new, lighter RailSAR cart puts less strain on the radar positioning hardware and allows the system to move smoothly along a specified portion of the rail. In previous papers, we have presented co-polarized SAR data collected using the ARL RailSAR. Recently, however, researchers at ARL have leveraged this asset to collect polarimetric data against multiple targets. This paper presents the SAR imagery resulting from these experiments and documents characteristics of certain target signatures that should be of interest to developers of automatic target detection (ATD) algorithms.
The Army Research Laboratory has constructed an indoor, rail-mounted, synthetic aperture radar (SAR) system capable of simulating airborne data collection geometries. The collection facility includes both a “building within a building” for through-the-wall measurements and a “sand pit” for buried-target measurements. While we collect background measurements for the purpose of clutter removal, the elimination of multi-path responses due to target emplacements presents a significant problem. These multipath effects can manifest themselves as artifacts in the processed SAR imagery— artifacts that were observed in data presented at last year’s Defense, Security and Sensing Radar Sensor Technology conference. In this paper, we present the results of additional data collections and analysis performed to identify the source of observed Rail-SAR artifacts. We analyze data collected using various target-emplacement scenarios and describe the procedures developed to eliminate artifacts in future Rail-SAR experiments. We examine results obtained both with and without the new measurement procedures in place.
Stepped-Frequency Radars (SFRs) have become increasingly popular with the advent of new technologies and increasingly congested RF spectrum. SFRs have inherently high dynamic range due to their small IF bandwidths, allowing for the detection of weak target returns in the presence of clutter. The Army Research Laboratory’s (ARL) Partnership in Research Transition program has developed a preliminary SFR for imaging buried landmines and improvised explosive devices. The preliminary system utilizes two transmit antennas and four receive antennas and is meant to act as a transitional system to verify the system’s design and imaging capabilities. The SFR operates between 300 MHz and 2000 MHz, and is capable of 1-MHz step-sizes. The SFR system will eventually utilize 16-receive channels and will be mounted on ARL’s existing Forward-Looking Ground Penetrating Radar platform, as a replacement for the existing Synchronous Impulse REconstruction (SIRE) radar. An analysis of the preliminary SFRs radio frequency interference mitigation, spectral purity dynamic range, and maximum detectable range is presented here.
The Army Research Laboratory (ARL) is developing an indoor experimental facility to evaluate and assess airborne synthetic-aperture-radar-(SAR)-based detection capabilities. The rail-SAR is located in a multi-use facility that also provides a base for research and development in the area of autonomous robotic navigation. Radar explosive hazard detection is one key sensordevelopment area to be investigated at this indoor facility. In particular, the mostly wooden, multi-story building houses a two (2) story housing structure and an open area built over a large sandbox. The housing structure includes reconfigurable indoor walls which enable the realization of multiple See-Through-The-Wall (STTW) scenarios. The open sandbox, on the other hand, allows for surface and buried explosive hazard scenarios. The indoor facility is not rated for true explosive hazard materials so all targets will need to be inert and contain surrogate explosive fills. In this paper we discuss the current system status and describe data collection exercises conducted using canonical targets and frequencies that may be of interest to designers of ultra-wideband (UWB) airborne, ground penetrating SAR systems. A bi-static antenna configuration will be used to investigate the effects of varying airborne SAR parameters such as depression angle, bandwidth, and integration angle, for various target types and deployment scenarios. Canonical targets data were used to evaluate overall facility capabilities and limitations. These data is analyzed and summarized for future evaluations. Finally, processing techniques for dealing with RF multi-path and RFI due to operating inside the indoor facility are described in detail. Discussion of this facility and its capabilities and limitations will provide the explosive hazard community with a great airborne platform asset for sensor to target assessment.
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
SAR image.
This paper investigates the variability of the human body radar signature, for both stationary targets (where we are
interested in the radar cross section) and moving targets (where we are interested in the Doppler response). The approach
in this paper introduces both mesh distortions and variable walking patterns, in order to predict changes in the radar
signature induced by morphological changes in the human meshes. The study is based entirely on computer simulations.
We start with a basic human mesh and use the Maya software package to articulate or distort the model. Realistic human
motion animation is obtained by using spatial coordinates from real motion capture data. The radar signature is obtained
by running a Finite Difference Time Domain-based electromagnetic solver. Results are presented as radar cross section
for stationary targets or Doppler spectrograms for moving targets.
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
proposed method.
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
tools.
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.
KEYWORDS: Sensors, Global Positioning System, Unattended ground sensors, Monte Carlo methods, Autoregressive models, Error analysis, Target detection, Time metrology, Sensor networks, Analytical research
We present a procedure wherein unattended ground sensors (UGSs) that are not equipped the GPS can locate their own positions by transmitting pulses and receiving retransmitted pulses from UGSs that are equipped the GPS. T The payoff of this approach is reduced cost for the network of UGSs. We show through simulation that the implementation of this procedure locates the sensors that do not have GPS with sufficient accuracy for the network of UGS to detect and locate moving targets.
We present a procedure for classification of targets by a network of distributed radar sensors deployed to detect, locate and track moving targets. Estimated sensor positions and selected positions of a target under track are used to obtain the target aspect angle as seen by the sensors. This data is used to create a multi-angle profile of the target. Stored target templates are then matched in the least mean square sense with the target profile. These templates were generated from radar return signals collected from selected targets on a turntable. Probabilities of correct classification obtained by a simulation of the classification procedure are given as functions of signal-to-noise ratios and errors in estimates of target and sensor locations.
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.
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