Through the Sensor Active Sonar Enhancement

This proposal describes a principled approach based on a proven technology base that unifies DCL Information Processing (IP). Our approach incorporates prior environmental information (e.g., spatial-temporal clutter map, sound speed, etc.) and adapts to in-situ environmental information to account for the effects of complex environmental conditions and other sources of clutter and confusables. A key aspect of our approach is employment of Bayesian techniques for statistical characterization of clutter response due to spatially and temporally varying environments. The characterization is represented by distribution functions to account for the inherent variability and uncertainty in environment and in clutter response. The estimated clutter distribution produces a likelihood ratio test statistic that is processed by IP detection and tracking modules. This process is referred to as normalization or whitening. A second key innovation is the use of environmental and clutter estimation filters to evolve and update the spatial distribution estimates over time. This closed-loop, data adaptive filter jointly improves system detection (PD/FA), tracking and classification through improved environmental and clutters solution and vice versa in a unified and seamless manner.