The Air Force Research Laboratory (AFRL) has an ongoing investigation to evaluate the behavior of Small Unmanned Aerial Vehicles (SAVs) and Micro Aerial Vehicles (MAVs) flying through an urban setting. This research is being conducted through the Cooperative Operations in UrbaN TERrain (COUNTER) 6.2 research and flight demonstration program. COUNTER is a theoretical and experimental program to develop the technology needed to integrate a single SAV, four MAVs, and a human operator for persistent intelligence, reconnaissance and surveillance for obscured targets in an urban environment. The research involves development of six-degree-of-freedom models for integration into simulations, modeling and integration of wind data for complex urban flows, cooperative control task assignment and path planning algorithms, video tracking and obstacle avoidance algorithms, and an Operator Vehicle Interface (OVI) system. The COUNTER concept and the contributing technologies will be proven via a series of flight tests and system demonstrations.
The first of six planned COUNTER flight demonstrations occurred in July of 2005. This demonstration focused on the simultaneous flight operations of both the SAV and the MAV while displaying their respective telemetry data on a common ground station (OVI). Current efforts are focused on developing the architecture for the Cooperative Control Algorithm. In FY 2006, the COUNTER program will demonstrate the ability to pass vehicle waypoints from the OVI station to the SAV and MAV vehicles. In FY 2007, COUNTER will focus on solutions to the optical target tracking (SAV) and obstacle avoidance (MAV) issues.
High-Range Resolution (HRR) radar modes have become increasingly important in the past few years due to the ability to form focused range profiles of moving targets with enhanced target-to-clutter ratios via Doppler filtering and/or clutter cancellation. To date, much research has been performed on using HRR radar profiles of both moving and stationary ground targets for Automatic Target Recognition (ATR) and Feature-Aided Tracking (FAT) applications. However, little work evaluating the correlation between moving versus stationary HRR profiles has been reported. This paper presents analytical comparisons between HRR profiles generated from a moving vehicle and profiles formed from Synthetic Aperture Radar (SAR) images of the identical stationary vehicle. The moving target HRR profiles are formed by integrating range-Doppler target images detected from clutter suppressed phase history data. The stationary target HRR profiles are formed from SAR imagery target chips by segmenting the target from clutter and reversing the image formation process. The purpose of this research is to identify which features, such as profile peaks, peak intensity, electrical length, among others, are common to profiles of the same target type and class and at the same imaging geometry.
This paper describes the DARPA Moving Target Feature Phenomenology (MTFP) data collection conducted at the China Lake Naval Weapons Center's Junction Ranch in July 2001. The collection featured both X-band and Ku-band radars positioned on top of Junction Ranch's Parrot Peak. The test included seven targets used in eleven configurations with vehicle motion consisting of circular, straight-line, and 90-degree turning motion. Data was collected at 10-degree and 17-degree depression angles. Key parameters in the collection were polarization, vehicle speed, and road roughness. The collection also included a canonical target positioned at Junction Ranch's tilt-deck turntable. The canonical target included rotating wheels (military truck tire and civilian pick-up truck tire) and a flat plate with variable positioned corner reflectors. The canonical target was also used to simulate a rotating antenna and a vibrating plate. The target vehicles were instrumented with ARDS pods for differential GPS and roll, pitch and yaw measurements. Target motion was also documented using a video camera slaved to the X-band radar antenna and by a video camera operated near the target site.
The present era of limited warfare demands that warfighters have the capability for timely acquisition and precision strikes against enemy ground targets with minimum collateral damage. As a result, automatic target recognition (ATR) and Feature Aided Tracking (FAT) of moving ground vehicles using High Range Resolution (HRR) radar has received increased interest in the community. HRR radar is an excellent sensor for potentially identifying moving targets under all-weather, day/night, long-standoff conditions. This paper presents preliminary results of a Veridian Engineering Internal Research and Development effort to determine the feasibility of using invariant HRR signature features to assist a FAT algorithm. The presented method of invariant analysis makes use of Lie mathematics to determine geometric and system invariants contained within an Object/Image (O/I) relationship. The fundamental O/I relationship expresses a geometric relationship (constraint) between a 3-D object (scattering center) and its image (a 1-D HRR profile). The HRR radar sensor model is defined, and then the O/I relationship for invariant features is derived. Although constructing invariants is not a trivial task, once an invariant is determined, it is computationally simple to implement into a FAT algorithm.
Automatic target recognition (ATR) and feature-aided tracking (FAT) algorithms that use one-dimensional (1-D) high range resolution (HRR) profiles require unique or distinguishable target features. This paper explores the use of Xpatch extracted scattering centers to generate synthetic moving ground target signatures. The goal is to develop a real-time prediction capability for generating moving ground target signatures to facilitate the determination of unique and distinguishable target features. The repository of moving ground target signatures is extremely limited in target variation, target articulation, and aspect and illumination angle coverage. The development of a real-time moving target signature capability that provides first order moving target signature will facilitate the development of features and their analysis. The proposed moving target signature simulation is described in detail and includes both the strengths and weaknesses of using a scattering center approach for generation of moving target signatures.
This paper is an initial exploration into the effects of range resolution on Automatic Target Recognition (ATR) algorithms based on High Range Resolution (HRR) signatures. The theoretical performance of a two-class, forced-decision classifier is used to quantify the effects of radar resolution on ATR performance. The classifier employed in this study is a forced-decision instantiation of the matched subspace classifier (MSC) developed under the DARPA TRUMPETS program. The paper also examines effects of range resolution on the separability of individual HRR profiles. This work is supported by DARPA/SPO under the MSTAR Enhancements (HBTI) program and in cooperation with AFRL/SNAA.
This study summarizes recent algorithmic enhancements made to the AFRL/SNAA Systems-Oriented High Range Resolution (HRR) Automatic Recognition Program (SHARP) in the areas of multiple-look updating and sensor fusion. The benefits in improved 1-D Automatic Target Recognition (ATR) performance resulting from these enhancements are quantified. The study incorporates a unique method of estimating Bayesian probabilities by exploiting the fact that 1-D range profiles formed from Moving and Stationary Target Acquisition and Recognition (MSTAR) target chips overlap in azimuth. Thus, multiple samples of range profiles exist for the same target at very similar viewing aspects, but from independent passes of the sensor. ATR performance using the Bayesian technique is characterized first for an updating architecture that fuses probabilities over a fixed number of looks and then makes a 'classify or reject' decision. A second proposed architecture that makes a 'classify, reject, or take another measurement' decision is also analyzed. For both postulated architectures, ATR performance enhancement over the SHARP baseline updating procedure is quantified.
Automatic target recognition (ATR) and feature-aided tracking (FAT) algorithms that use one-dimensional (1-D) high range resolution (HRR) profiles require unique or distinguishable target features. This paper explores the use of statistical measures to quantify the separability and stability of ground target features found in HRR profiles. Measures of stability, such as the mean and variance, can be used to determine the stability of a target feature as a function of the target aspect and elevation angle. Statistical measures of feature predictability and separability, such as the Fisher and Bhattacharyya measures, demonstrate the capability to adequately predict the desired target feature over a specified aspect angular region. These statistical measures for separability and stability are explained in detail and their usefulness is demonstrated with measured HRR data.
This paper presents the performance of a multi-class, template-based, system-oriented High Range Resolution (HRR) Automatic Target Recognition (ATR) algorithm for ground moving targets. The HRR classifier assumes a target aspect estimate derived from the exploitation of moving target indication (MTI) mode target tracking to reduce the template search space. The impact of the MTI tracker target aspect estimate accuracy on the performance and robustness of the HRR ATR is investigated. Next, both individual and hybrid MTI/HRR and Synthetic Aperture Radar (SAR) model-based ATR algorithm results are presented. The hybrid ATR under consideration assumes the coordination of a multimode sensor to provide classification or continuous tracking of targets in a move- stop-move scenario. That is, a high-value moving target is tracked using the GMTI mode and its heading estimated. As the indicated target stops, the last GMTI tracker update is used to aid the SAR mode ATR target acquisition and classification. As the target begins to move again, the MTI-assisted HRR ATR target identification estimates are fused with the previous SAR ATR classification. The hybrid MTI/SAR/HRR ATR decision- level fusion provides a method for robust classification and/or continuous tracking of targets in move-stop-move cycles. Lastly, the baseline HRR ATR performance is compared to a QuickSAR (short dwell or non-square pixel SAR) ATR algorithm for varying cross-range resolutions.
High range resolution (HRR) radar is important for its all- weather, day/night, long standoff capability. Additionally, it is an excellent sensor for identifying moving ground targets because it produces high resolution target signatures and because targets can be separated from ground clutter using Doppler processing. Ongoing research under the System Oriented HRR Automatic Recognition Program has led to an increased understanding of the HRR data, the target separability, and a baseline assessment of target recognition algorithms using template based approaches.
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