Magnetic detection of submarines, mines and other objects of interest has continued to improve in both sensitivity of the sensors and reduction in the complexity and cost of sensor devices. Sensors alone, regardless of how far science is able to improve their abilities, will not solve the problem. The Navy will have to find processing solutions capable of quickly and cost-effectively highlighting objects of interest from magnetic sensor information. Brainlike Surveillance Research, Inc, proposes to develop 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 algorithm that learns baseline parameters and correlations automatically and continuously. The kernel operates efficiently, to the point of being deployable on remote sensor arrays. Along with the proposed system, Brainlike offers novel analysis methods that focus on increasing target identification rates, reducing false alarms, and automating the analysis process, resulting in reduced costly incidents, false alarm costs, and staffing requirements.
Keywords: Magnetic Sensors, Anti-Submarine Warfare, Mine Warfare, Auto-Adaptive Estimation, False Alarm Reduction, Increased Warning Lead Time, Reduced Surveillance Costs
