&lt;title&gt;A simulation system for feature-aided tracking research&lt;/title&gt;

Musick, Stanton; Sherwood, John U.; Piatt, Teri L.; Carlson, Neal A.

doi:10.1117/12.548005

Cited by 5 publications

(6 citation statements)

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“…1 was produced using a public-domain database called "Feature-Aided Tracking of Stop-move Objects (FATSO)" generated by the Sensor Directorate of the Air Force Research Laboratory [7]. The FATSO was used as shown in Fig.…”

Section: Pose Estimation In Cluttermentioning

confidence: 99%

A Net Track Solution to Pose-Angular Tracking of Maneuvering Targets in Clutter with HRR Radar

Yang¹,

Blasch

Garber³

et al. 2007

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

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Maintaining track continuity is a challenging task for large area persistent surveillance with high target density, rapid target maneuvering, and low target-to-clutter ratio. When the sensor revisit interval is long, measurement-to-track association becomes rather difficult. Although maneuvering targets can be discriminated via Doppler processing, clutter still masks moving targets at certain aspect angles. This creates a blind zone for GMTI (ground moving target indicator) radar, which may extend as large as 25% of the azimuth angles. For high-value targets that may undergo evasive maneuvers, a net track solution coordinating multiple looks from a netted set of sensors is set forth in this paper to ensure continuous monitoring. Target visibility therefore needs to be factored into netted radar platform coordination and sensor systems management. For ground targets, their heading is mostly aligned with the longitudinal axis. The target pose thus carries kinematic information about the direction of velocity vector that can be used to assist tracking of maneuvering targets. In this paper, the range profiles of high range resolution (HRR) radar are used for pose angular estimation of maneuvering targets in clutter. A pose-sensitive classifier provides matching scores that are converted into likelihoods and updated over time in a probabilistic manner. Pose angular tracking from individual sensors in the network is fused to cover the blind zone encountered by individual sensors. A software simulation environment that produces range profiles based on the RF signatures of moving targets is used in the study. Simulation results for pose angular tracking in clutter are presented to illustrate the net track solution concept and its performance.

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Section: Pose Estimation In Cluttermentioning

confidence: 99%

A Net Track Solution to Pose-Angular Tracking of Maneuvering Targets in Clutter with HRR Radar

Yang¹,

Blasch

Garber³

et al. 2007

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

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“…Notice the low level of detail present in this generic set. 1 The RSP generates RCS, HRR profile, and SAR signatures from the targets' reflective primitive models. Electromagnetic scattering analytic solutions are applied online for each reflector primitive in a model to calculate phase center data.…”

Section: Fatso Generic Target Modelsmentioning

confidence: 99%

“…To encourage further research in this area and provide for diverse investigative use, the Sensor's Directorate of the Air Force Research Laboratory (AFRL/SN) has designed a challenge problem called Feature-Aided Tracking of Stop-move Objects (FATSO) 1 . FATSO's testbed system is comprised of a database, software, and documentation that are organized into two major components: 1) a Signature Generation System (SGS), and 2) a simulation system (MTrack) with an embedded feature-aided tracker.…”

Section: Introductionmentioning

confidence: 99%

Radar signature generation for feature-aided tracking research

Piatt

Sherwood

Musick

2005

Algorithms for Synthetic Aperture Radar Imagery XII

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Accurately associating sensor kinematic reports to known tracks, new tracks, or clutter is one of the greatest obstacles to effective track estimation. Feature-aiding is one technology that is emerging to address this problem, and it is expected that adding target features will aid report association by enhancing track accuracy and lengthening track life. The Sensor's Directorate of the Air Force Research Laboratory is sponsoring a challenge problem called Feature-Aided Tracking of Stop-move Objects (FATSO). The long-range goal of this research is to provide a full suite of public data and software to encourage researchers from government, industry, and academia to participate in radar-based feature-aided tracking research. The FATSO program is currently releasing a vehicle database coupled to a radar signature generator. The completed FATSO system will incorporate this database/generator into a Monte Carlo simulation environment for evaluating multiplatform/multitarget tracking scenarios. The currently released data and software contains the following: eight target models, including a tank, ammo hauler, and self-propelled artillery vehicles; and a radar signature generator capable of producing SAR and HRR signatures of all eight modeled targets in almost any configuration or articulation. In addition, the signature generator creates Z-buffer data, label map data, and radar cross-section prediction and allows the user to add noise to an image while varying sensor-target geometry (roll, pitch, yaw, squint). Future capabilities of this signature generator, such as scene models and EO signatures as well as details of the complete FATSO testbed, are outlined.

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“…In the present case, we have M = 3, H 1 11), we use the same transition probability matrix as the IMM algorithm of Eq. (7).…”

Section: Bayesian-markov Estimator Of Turning Maneuversmentioning

confidence: 99%

“…Feature-aided tracking remains to be an area of active search, to support which a public-domain database called "Feature-Aided Tracking of Stop-move Objects (FATSO)" has been generated by the Sensor Directorate of the Air Force Research Laboratory (AFRL/SN) [1]. Compared to conventional tracking with post-detection position observables (i.e., range and bearing), feature-aided tracking (FAT) expands at least in two aspects:…”

Section: Introductionmentioning

confidence: 99%