In situ learning for underwater object recognition
Department of Defense
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Small Business Information
3 Phoenix, Inc.
13135 Lee Jackson Hwy, Suite 220, Fairfax, VA, 22033
Socially and Economically Disadvantaged:
AbstractSea mines are a cost-effective method for hostile forces to attempt to neutralize assets of the U.S. Navy by limiting mobility and creating delay. Mine detection, classification and localization (DCL) is very challenging in littoral environments due to the high clutter, increased background, and dense multipath. 3 Phoenix, Inc. has developed an innovative approach for automatic target detection and classification of sea mines and other underwater targets of interest. The proposed algorithm robustly adapts to changes in environment and has the potential for dramatically reducing false alarm rate, while still maintaining a high probability of detection and classification. A novel, efficient method of training the classifier is formulated and retraining for adaptation is performed intrinsically with weight optimization. The algorithm is generalized to work over several sensor types and sensor modalities. The proposed algorithm has the potential to reduce operator load while reducing false positives in classification.
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