In-Situ Adaptation For Underwater Target Detection and Classification Using An Information Theoretic Approach Navy SBIR FY2009.1 Sol No.: Navy SBIR FY2009.1 Topic No.: N091-066 Topic Title: In-Situ Adaptation For Underwater Target Detection and Classification Using An Information Theoretic Approach Proposal No.: N091-066-0512 Firm: Information Systems Technologies, Inc. 5412 Hilldale Court Fort Collins, Colorado 80526 Contact: M. Azimi-Sadjadi Phone: (970) 224-2556 Web Site: www.infsyst.biz Abstract: A critical need of the U.S. Navy is the development of a reliable, efficient and robust underwater target detection and classification system that can operate in real-time with various sonar systems and in different environmental and operating conditions. To maintain performance in such conditions, new solutions are needed to update the detection and classification systems in-situ in response to environmental and operational changes. The main goal of this Phase I research is to develop innovative solutions that offer in-situ learning ability for classification and possible identification of underwater targets using (a) a model-reference mechanism that incorporates input/output relations within a set of a new samples with class/within-class labels and confidence scores, (b) a relevance-feedback mechanism that attempts to capture expert operators high-level decision-making concepts via operators feedback, (c) an information-theoretic selective sampling method to extract the most informative training samples from the new environment, and (d) demonstration of the effectiveness of the algorithms on sonar data sets. The unique advantage of our proposed solutions is the ability to offer system flexibility while preserving the stability of the previously learnt information. Additionally, the system is simple and amenable for real-time implementation on a wide variety of sensor platforms. Benefits: This Phase I research leads to the development of innovative underwater target detection and classification methods that can successfully operate in varying environmental and operational conditions. The results of this research will be extremely beneficial to various DoD programs since not only it responds to the current requirements for MCM systems but also addresses the critical needs in future combat systems (FCS). Military is interested in the development of in-situ feature extraction, detection and classification methods for a wide variety of missions such as landmine and IED screening in different terrain, surveillance and remote sensing, monitoring environmental and weather conditions for mission planning on the battlefield. Other emerging areas of widespread application for in-situ learning systems include: adaptable image and video retrieval systems for law enforcement agencies and security systems, geological surveys, and underwater exploration. Thus, the result of this research would find a wide market encompassing military, environmental, and commercial arenas. Return