Clutter Removal and Substantially Improved Submarine and Mine Detection Through Affordable

This proposal offers a System for improving decisive inferences based on Air Deployable Active Receive (ADAR) sonar, in a form that is suitable for deployment on ADAR sonobuoys. Anticipated near-term improvements include better target recognition and reduced operator fatigue, brought about through improved ping data filtering on ADAR aircraft. Anticipated long-term improvements include increased sonobuoy persistence and decreased telemetry cost, delivered through sonobuoy-based target detection and telemetry control. Sonar detection of submarines, mines and other threats has continued to improve in both sensitivity of the sensors and reduction in the complexity and cost of sensor devices. Sensors alone will not produce optimal improvements. In addition, solutions capable of quickly and cost-effectively highlighting threats within continuously changing clutter backgrounds will be required. Brainlike Surveillance Research, Inc, offers a novel estimation system that adapts sensor data for improved target identification -- automatically, efficiently, and adaptively. Output from the system shows anomalies clearly, removes background clutter effectively, adapts to changing conditions automatically, and improves results from complementary classifiers substantially. The proposed system will run a novel, efficient kernel process that is deployable on remote sensor arrays.