The DARPA/AFRL "Moving and Stationary Target Acquisition and Recognition" (MSTAR) program is developing a modelbased vision approach to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The motivation for this work is to develop a high performance ATR capability that can identify ground targets in highly unconstrained imaging scenarios that include variable image acquisition geometry, arbitrary target pose and configuration state, differences in target deployment situation, and strong mtra-class variations. The MSTAR approach utilizes radar scattering models in an on-line hypothesize-and-test operation that compares predicted target signature statistics with features extracted from image data in an attempt to determine a "best fit" explanation of the observed image. Central to this processing paradigm is the Search algorithm, which provides intelligent control in selecting features to measure and hypotheses to test, as well as in making the decision about when to stop processing and report a specific target type or clutter. Intelligent management of computation performed by the Search module is a key enabler to scaling the model-based approach to the large hypothesis spaces typical ofrealistic ATR problems. In this paper, we describe the present state of design and implementation ofthe MSTAR Search engine, as it has matured over the last three years of the MSTAR program. The evolution has been driven by a continually expanding problem domain that now includes 30 target types, viewed under arbitrary squint/depression, with articulations, reconfigurations, revetments, variable background, and up to 30% blocking occlusion. We believe that the research directions that have been inspired by MSTAR's challenging problem domain are leading to broadly applicable search methodologies that are relevant to computer vision systems in many areas.
The Air Force Research Laboratory's Sensor Directorate is developing the next step in target recognition, a continuous identification capability. This capability consists of a wide range of algorithms, sensor modes and technologies that work in concert to contribute to the overall goal of identification ofmoving as well as stationary targets. Three major pieces ofthis emerging capability are stationary identification, correlation ofthis information with tracks and the moving target recognition technologies. Although a brief discussion of each ofthese pieces will be provided in this paper, the concentration will be on the actual algorithms and technologies ofthe moving target recognition exploiting High Range Resolution (HRR) radar mode and how this complements Ground Moving Target Indication (GMTI) tracking and Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). This paper will expand on the overall continuous ID vision, work performed under several efforts, new sources of HRR data, and how this data will push the state ofthe art.The advantages of continuous ID should be obvious to the warfighter in many dimensions. As a command and control structure it is very difficult to identify and target time critical targets especially when the identification process is limited to stationary targets. These technologies will provide a wider range oftargeting options so the enemy has no sanctuary in moving, setting up and in hiding.
JOSEPH R. DIEMUNSCH VICTOR R. CLARK WRIGHT LABORATORY WPAFB Off 45433 WL/AAZw-2
This paper describes the Sensor to Shooter Information Fusion for Rapid Targeting (SSIFRT) program. The objective ofthis program is to design, develop, test, and demonstrate the fusion of intelligence, surveillance, and reconnaissance (TSR) data with on-board sensor data. This decentralized information fusion system will take advantage ofboth on-board tactical platform and off-board sensor data to generate a high performance identification capability. The algorithm development will address Automatic Target Recognition (ATR), ground target tracking, target cueing, and registration of imagery residing on both ground station (off-board) and tactical aircraft (on-board) systems. Analysis of data link and processing requirements/capabilities will be performed to determine an on-board and off-board fusion architecture.The off-board component would be targeted for ground station applications where multiple sources of information will come together. The off-board fusion algorithm employs a Bayesian approach to integrate information from multiple image sources such as SAR, EO, and FLIR as well as non-image based intelligence sources such as Ground Moving Target Indicators (GMTI). Model-based ATR technology will be an important module of this off-board fusion system for the extraction of target information from image data sources. Compression techniques and innovative representations of information will be investigated so that information can be transferred to the shooter platform using existing or planned communication channels. Finally, the tactical platform's on-board system will incorporate a fused feature methodology that utilizes off-board cues and on-board SAP. and FLIIR imagery for final on-board target identification. INTRODUCTIONThe SSIFRT project has very ambitious goals in terms ofboth image ATR and data fusion. The goals will be achieved by leveraging technology from a wide range of existing programs. The baseline SSIFRT program utilizes the off-board fusion system from the Information Fusion OnlOff-board Avionics (INFO) program, an AFRL Phase II SBIR, which has developed an all-source information fusion architecture for integrating information from multiple intelligence sources, including image data from SAR, FLIR, and potentially EO sources. Within this architecture, image data is preprocessed to
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