SONALYSTS INC — Department of Defense SBIR Phase I: N171-093

Description

Our proposed solution will develop a derivative adaptive filter drawn from Control Theory to continuously evaluate threat submarine contact data and negative search data to generate the best fit estimated contact location derived by weighting each member of a set of estimated threat submarine tracks. These estimated threat submarine track inputs to the adaptive filter will be generated from two types of data. Hard data, such as threat submarine historical tracks and operating characteristics including transit and maximum speed, can be easily converted to locational information. Soft data requires consolidation and conversion using ontologically-based rule sets and fuzzy logic/machine learning to transform data, such as the tension level, with the threat country to create a set of plausible target tracks based on this information. The adaptive filter will output a heat map to indicate the likelihood of future locations for the threat submarine.