Aquatic Vessel ATR using Structural Traits (AVAST) Navy SBIR FY2008.1 Sol No.: Navy SBIR FY2008.1 Topic No.: N08-044 Topic Title: Aquatic Vessel ATR using Structural Traits (AVAST) Proposal No.: N081-044-0140 Firm: Charles River Analytics Inc. 625 Mount Auburn Street Cambridge, Massachusetts 02138 Contact: Ross Eaton Phone: (617) 491-3474 Web Site: www.cra.com Abstract: To maximize situational awareness and survivability of Navy assets in littoral environments, Navy submarines must be able to quickly distinguish between hostile targets and similarly-sized non-hostile vessels. Currently, skilled personnel determine if each contact is hostile. This approach is limited by available operators, the classification speed and accuracy of each operator, the number of recognizable ship types, and operator attention spans. Automation is the key to reducing operator workload, and vision-based automatic target recognition (ATR) techniques will allow less skilled personnel to more accurately identify a wider variety of marine targets without loss of focus in a variety of challenging conditions. Model-based classification methods hold much promise for solving the ship identification problem in the varying conditions faced by Navy submarines. We propose a model-based ATR approach called Aquatic Vessel ATR using Structural Traits, or AVAST. AVAST extracts ship silhouettes from images, and derives a skeleton to model the ship's main structures. The relative geometry and real-world measurements of these structures are computed and then used to identify matching ship types in a database of known vessels in real-time (less than 1 second per target). The matching ship types are then displayed for operator confirmation and, if necessary, refinement. Benefits: Determining the threat posed by ships in littoral environments, where Navy vessels are most vulnerable to covert attacks, is a vital task for fleet security. Similarly, port security is a critical concern for both the Department of Homeland Security and for private shipyards. AVAST promises to produce a set of algorithms that is capable of performing the ship recognition needed for military, domestic, and private harbor security, thereby creating business opportunities for Charles River Analytics in new application areas. The proposed work will feed directly into our Automatic Target Recognition core business while allowing us to address the immediate and pervasive need for ship classification methods. Return