The objective of the present investigation is to use radar data to detect targets situated on or
under a road surface, and, at the same time, minimize the number of false alarms. The data used
here have been collected by the Army Research Laboratory (ARL) Synchronous Impulse
Reconstruction (SIRE) Radar. These data have been processed at different ranges from the radar,
at different depression angles, and with different resolution. This has been achieved by
integrating the data collected during the forward motion of the radar along the road. As a result,
it has been possible to produce a series of images of the road in front of the radar at progressively
better resolution. We show how the exploitation of the different behavior of clutter and targets at
different resolution allows higher rates of target detection at lower false alarm rate than
The Army Research Laboratory (ARL) has, in the past, demonstrated the effectiveness of low frequency,
ultrawideband radar for detection of slow-moving targets located behind walls. While these initial results
were promising, they also indicated that sidelobe artifacts produced by moving target indication (MTI)
processing could pose serious problems. Such artifacts induced false alarms and necessitated the
introduction of a tracker stage to eliminate them. Of course, the tracker algorithm was also imperfect, and
it tended to pass any persistent, nearly collocated false alarms.
In this work we describe the incorporation of a sidelobe-reduction technique-the randomized linear
receiver array (RA)-into our MTI processing chain. To perform this investigation, we leverage data
collected by ARL's synchronous impulse reconstruction (SIRE) radar. We begin by calculating MTI
imagery using both the non-random and randomized array methods. We then compare the sidelobe levels
in each image and quantify the differences. Finally, we apply a local-contrast target detection algorithm
based on constant false alarm rate (CFAR) principles, and we analyze probabilities of detection and false
alarm for each MTI image.
Moving target indication (MTI) algorithms often operate within a relatively narrow frequency band
relying on Doppler processing to detect moving targets at long standoff ranges. At these standoff ranges,
received wavefronts impinging on a linear array can be considered planar, enabling implementation of a
variety of phase-based beam-forming techniques. At near ranges, however, the plane-wave assumption no
longer holds. We describe enhancements to an impulse-based, low-frequency, ultra-wideband, moving-target
imaging system for near-range, through-the-wall MTI. All MTI image processing is performed in
the time domain using a change detection (CD) paradigm. We discuss how MTI image quality can be
increased through the introduction of randomized linear arrays. After describing the process in detail, we
present results obtained using data collected by an impulse-based, low frequency, ultra-wideband system.
Previously, we developed a moving target indication (MTI) processing approach to detect and track slow-moving targets
inside buildings, which successfully detected moving targets (MTs) from data collected by a low-frequency, ultra-wideband
radar. Our MTI algorithms include change detection, automatic target detection (ATD), clustering, and
tracking. The MTI algorithms can be implemented in a real-time or near-real-time system; however, a person-in-the-loop
is needed to select input parameters for the clustering algorithm. Specifically, the number of clusters to input into the
cluster algorithm is unknown and requires manual selection. A critical need exists to automate all aspects of the MTI
processing formulation. In this paper, we investigate two techniques that automatically determine the number of clusters:
the adaptive knee-point (KP) algorithm and the recursive pixel finding (RPF) algorithm. The KP algorithm is based on a
well-known heuristic approach for determining the number of clusters. The RPF algorithm is analogous to the image
processing, pixel labeling procedure. Both algorithms are used to analyze the false alarm and detection rates of three
operational scenarios of personnel walking inside wood and cinderblock buildings.
This paper presents a time-domain, Moving-Target-Indication (MTI) processing formulation for detecting slow-moving
personnel behind walls. The proposed time-domain MTI processing formulation consists of change detection and
tracking algorithms. We demonstrate the effectiveness of the MTI processing formulation using data collected by the
Army Research Laboratory's (ARL's), Ultra-Wideband (UWB), Synchronous Impulse Reconstruction (SIRE) radar.
During the collection of the data, the SIRE radar remains stationary and is positioned broadside to the wall and 38
degrees off the broadside position. We have collected data for multiple operational scenarios including: personnel
walking inside wood and cinderblock structures, personnel walking in linear and non-linear trajectories, and multiple
personnel walking within the building structure. We analyze the characteristics of moving target signatures for the
multiple operational scenarios and describe the detection and tracking algorithms implemented to exploit them.
This paper analyzes the application of ultra-wideband ground-penetrating radar (GPR) in a down-looking configuration
for the detection of buried targets. As compared to previous studies, where target detection algorithms have been
developed based on the radar range profiles alone (pre-focus data), we investigate the potential performance
improvement by forming synthetic aperture radar (SAR) images of the targets. This becomes important in scenarios with
small signal-to-noise or signal-to-clutter ratios. Our three-dimensional (3-D) image formation algorithm is based on the
backprojection technique. We apply this method to radar scattering data obtained through computer simulation by the
finite-difference time-domain (FDTD) technique. Our analysis demonstrates the advantages of using focused SAR
images versus the pre-focus range profiles. We also perform a parametric study of several physical factors that could
affect the image quality.
The Army Research Laboratory (ARL) has been engaged in an effort to support the "See through
the Wall" initiative. As part of the effort, we have explored the possibilities and challenges of
using data collected by small groups of men - each man equipped with a small, hand-held, Radio
Frequency (RF) sensor - to image the interior of buildings. We examine various multi-static
combinations of sensors, especially in configurations that allow for imaging a room from
different viewing angles, and we demonstrate the capability of these approaches for providing
comprehensive information about a building's interior. We examine the consequences of errors in
the assumed position of the RF sensors, and we analyze the effects of the degradation of signal
coherence due to mistiming. Simulation results are provided to show the potential of this type of
sensor arrangement in such a difficult, urban environment.
In support of the U.S. Army's need for intelligence on the configuration, content, and human presence inside enclosed areas (buildings), the Army Research Laboratory is currently engaged in an effort to evaluate RF sensors for the "Sensing Through The Wall" initiative (STTW).Detection and location of the presence of enemy combatants in urban settings poses significant technical and operational challenges. This paper shows the potential of hand held RF sensors, with the possible assistance of additional sources like Unattended Aerial Vehicles (UAV), Unattended Ground Sensors (UGS), etc, to fulfill this role. In this study we examine both monostatic and multistatic combination of sensors, especially in configurations that allow the capture of images from different angles, and we demonstrate their capability to provide comprehensive information on a variety of buildings. Finally, we explore the limitations of this type of sensor arrangement vis-a-vis the required precision in the knowledge of the position and timing of the RF sensors. Simulation results are provided to show the potential of this type of sensor arrangement in such a difficult environment.
A need exists for greater situational awareness at the lower echelons of the Army. Radar Frequency (RF) sensors on small, lightweight Unmanned Aerial Vehicles (UAV) could provide lower echelon commanders with all-weather reconnaissance, early warning, and target acquisition; however, the designs of these RF sensors are limited by the projected size and weight restrictions on the payload for a class II UAV. Consequently, these designs may favor combining simple RF sensor hardware with digital-signal processing (DSP) solutions over more sophisticated radar hardware. In this paper, we show the potential of simple, low cost RF sensors with hemispherical antenna coverage to overcome these limitations. The proposed RF sensor system used DSP and pre-defined UAV flight pattern to detect and track moving targets from range and Doppler information. Our objective is to conceive and model a suite of software options that, by combining UAV flight patterns and processing algorithms, will be able to detect and track moving targets. In order to accomplish this, we are building a simulation that uses sensor models, target models, and battlefield dynamics to predict the targeting capabilities of the RF sensor system. We will use this simulation (1) to determine the tradeoffs between sensor complexity (and cost) and the military significance of the information gathered, and (2) to describe sensor error budgets for endgame lethality models
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.
Trained algorithms are required for detecting stationary targets with practical real-beam radars. The parameters of these algorithms are unique to each site or clutter class. A problem arises when an algorithm trained on one clutter class is applied, perhaps inadvertently, to another class. In this case, the performance of the system can degrade to an unacceptable level. We have developed a system that adapts, online, the parameters of the algorithm to the
encountered clutter type. This system consists of two neural networks - one for adapting the coefficients of the algorithm and the other for adapting the threshold level.
An important element of Army transformation efforts is the development of significantly improved munitions. Amongst aggressive goals is a capability to achieve "one shot... at least one kill..." for non-line-of-sight encounters at extended ranges. This objective places unprecedented requirements on sensor technology. A network of sensors must be able to detect, locate, and track the targets; estimate their positions; and periodically uplink that information to the munition, while it is in flight. The Army Research Laboratory formulated the Distributed Sensor Network for Dynamic Retargeting program to address these target-acquisition and dynamic retargeting issues.