Jeffrey Sichina

Obstacle avoidance and concealed target detection using the Army Research Lab ultra-wideband synchronous impulse reconstruction (UWB SIRE) forward imaging radar

The U.S. Army Research Laboratory (ARL), as part of a mission and customer funded exploratory program, has developed a new low-frequency, ultra-wideband (UWB) synthetic aperture radar (SAR) for forward imaging to support the Armys vision of an autonomous navigation system for robotic ground vehicles. These unmanned vehicles, equipped with an array of imaging sensors, will be tasked to help detect man-made obstacles such as concealed targets, enemy minefields, and booby traps, as well as other natural obstacles such as ditches, and bodies of water. The ability of UWB radar technology to help detect concealed objects has been documented in the past and could provide an important obstacle avoidance capability for autonomous navigation systems, which would improve the speed and maneuverability of these vehicles and consequently increase the survivability of the U. S. forces on the battlefield. One of the primary features of the radar is the ability to collect and process data at combat pace in an affordable, compact, and lightweight package. To achieve this, the radar is based on the synchronous impulse reconstruction (SIRE) technique where several relatively slow and inexpensive analog-to-digital (A/D) converters are used to sample the wide bandwidth of the radar signals. We conducted an experiment this winter at Aberdeen Proving Ground (APG) to support the phenomenological studies of the backscatter from positive and negative obstacles for autonomous robotic vehicle navigation, as well as the detection of concealed targets of interest to the Army. In this paper, we briefly describe the UWB SIRE radar and the test setup in the experiment. We will also describe the signal processing and the forward imaging techniques used in the experiment. Finally, we will present imagery of man-made obstacles such as barriers, concertina wires, and mines.

Sensing through the wall imaging using the Army Research Lab ultra-wideband synchronous impulse reconstruction (UWB SIRE) radar

The U.S. Army Research Laboratory (ARL), as part of a mission and customer funded exploratory program, has developed a new low-frequency, ultra-wideband (UWB) synthetic aperture radar (SAR). The radar is capable of penetrating enclosed areas (buildings) and generating SAR imagery. This supports the U.S. Armys need for intelligence on the configuration, content, and human presence inside these enclosed areas. The radar system is mounted on a ground based vehicle traveling along the road and is configured with an array of antennas pointing toward the enclosed areas of interest. This paper will describe an experiment conducted recently at Aberdeen Proving Ground (APG), Maryland. In this paper we briefly describe the UWB SIRE radar and the test setup in the experiment. We will also describe the signal processing and the image techniques used to produce the SAR imagery. Finally, we will present SAR imagery of the building and its internal structure from different viewing directions.

Microwave underground propagation and detection

The detection of buried targets has been a problem of significant interest for decades, with microwave-based sensing constituting an important tool. In this paper, we review the basic issues that characterize microwave-based subsurface sensing. Issues considered include the use of microwaves in the context of an airborne synthetic aperture radar, as well for radars deployed close to the air-soil interface. Rough-surface induced clutter is also discussed. Particular examples are presented for detection of land mines and unexploded ordnance.

Physics-based detection of targets in SAR imagery using support vector machines

Radar scattering from an illuminated object is often highly dependent on the target-sensor orientation. In conjunction with physics based feature extraction, the exploitation of aspect-dependent information has led to successful improvements in the detection of tactical targets in synthetic aperture radar (SAR) imagery. While prior work has attempted to design detectors by matching them to images from a training set, the generalization capability of these detectors beyond the training database can be significantly improved by using the principle of structural risk minimization. In this paper, we propose a detector based on support vector machines that explicitly incorporates this principle in its design, yielding improved detection performance. We also introduce a probabilistic feature-parsing scheme that improves the robustness of detection using features obtained from a two-dimensional matching-pursuits feature extractor. Performance is assessed by considering the detection of tactical targets concealed in foliage, using measured foliage-penetrating SAR data.

Multi-aspect detection of surface and shallow-buried unexploded ordnance via ultra-wideband synthetic aperture radar

An ultra-wideband (UWB) synthetic aperture radar (SAR) system is investigated for the detection of former bombing ranges, littered by unexploded ordnance (UXO). The objective is detection of a high enough percentage of surface and shallow-buried UXO, with a low enough false-alarm rate, such that a former range can be detected. The physics of UWB SAR scattering is exploited in the context of a hidden Markov model (HMM), which explicitly accounts for the multiple aspects at which a SAR system views a given target. The HMM is trained on computed data, using SAR imagery synthesized via a validated physical-optics solution. The performance of the HMM is demonstrated by performing testing on measured UWB SAR data for many surface and shallow UXO buried in soil in the vicinity of naturally occurring clutter.

Detection algorithms for ultrawideband foliage-penetration radar

The Army Research Laboratory (ARL), as part of its mission- funded exploratory development program, has been evaluating the use of a low-frequency, ultra-wideband imaging radar to detect tactical vehicles concealed by foliage. An instrumentation-grade measurement system has been designed and implemented by ARL. Extensive testing of this radar over the preceding 18 months has led to the establishment of a significant and unique data base of radar imagery. We are currently using these data to develop target detection algorithms which can aid an operator in separating vehicles of interest from background. This paper provides early findings from the algorithm development effort. To date, our efforts have concentrated on identifying computationally simple strategies for canvassing large areas for likely target occurrences--i.e., prescreening of the imagery. Phenomenologically-sound features are being evaluated for discrimination capability. Performance assessments, in terms of receiver operating characteristics, detail detection capabilities at various false alarm rates.

Mine field detection algorithm utilizing data from an ultrawideband wide-area surveillance radar

The Army Research Laboratory (ARL), as part of its mission- funded applied research program, has been evaluating the utility of a low-frequency, ultra-wideband (UWB) imaging radar to detect obscured targets such as vehicles concealed by foliage and objects buried underground. This paper concentrates on a specific area of great interest to the Army: the reliable detection of surface and buried mines. Measurement programs conducted at Yuma Proving Ground and elsewhere have yielded a significant and unique database of extremely wideband and (in many cases) fully polarimetric data. We will review recent findings from ARLs modeling, phenomenology and detection efforts. We also included a discussion of an end-to-end detection strategy that has been trained and tested against a significant data set. Performance assessments are included that detail detection rates versus false alarm levels.

Wide-area detection of land mines and unexploded ordnance

Advanced electromagnetic modelling tools are discussed, focused on sensing surface and buried land mines and unexploded ordnance, situated in a realistic soil environment. The results from these forward models are used to process scattered-field data, for target detection and identification. We address sensors directed toward the wide-area-search problem, for which one is interested in detecting a former mine field or bombing range. For this problem class we process data measured from an actual airborne radar system. Signal-processing algorithms applied include Bayesian processing and a physics-based hidden Markov model.

Recent MTI experiments using ARL's synchronous impulse reconstruction (SIRE) radar

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 systems utility by imaging static scenes. In this paper we address the moving target indication (MTI) problem, and we demonstrate the impulse-based systems 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.

Low-frequency ultra-wideband synthetic aperture radar 1995 BoomSAR tests

The Army Research Laboratory has been investigating the potential of ultra-wideband synthetic aperture radar (UWB SAR) technology to detect and classify targets embedded in foliage or in the ground. The UWB foliage penetration (FOPEN) radar program has been extended to include the evaluation of ground penetration (GPEN) radar technology. ARL is investigating these problems by collecting high quality, precision data to support phenomenological investigations of electromagnetic wave propagation through dielectric media. Understanding the phenomenology of wave/target/clutter interactions supports the development of algorithms for automatic target recognition. The latest version of the radar developed by ARL is the UWB BoomSAR mounted on a 150-ft-high mobile boom lift. The BoomSAR is a mobile platform that can travel to various sites to collect target data in a variety of clutter scenarios. This paper provides a description of the boom radar system and imagery from data collections at Aberdeen Proving Ground, MD, and Yuma Proving Ground, AZ.