Through the Sensor Active Sonar Enhancement

This Through the Sensor Active Sonar Enhancement SBIR aims to develop algorithms for in-situ adaptive sonar performance prediction. These algorithms will provide spatially and temporally varying estimates for terms in the sonar performance prediction equation such as clutter, signal-to-noise ratio (SNR), probability of detection (PD), and false alarm rate (FAR). The primary purpose of these estimates is to provide statistical distributions for the terms in the sonar equation. These distributions, referred to as sonar equation maps (SE-MAPs), characterize the expected value and its uncertainty over space and time and can be used for sonar performance prediction for system design and for real-time operation of warfare systems. Operationally, the SE-MAPs will used as input to space-time adaptive processing (STAP) algorithms for data normalization (also referred to as whitening) and suppression of clutter and other unwanted noise sources. Data normalization is a critical processing component of Information Processing (IP) streams, providing the means for effective and robust target detection, tracking and classification. The SE-MAPs can combine historical data from disparate sensor surveillance platforms to produce fused geospatial-temporal distributions for use by operational sensor systems and for system development and design.