Surface Ship, Hull Mounted, Mine Avoidance Sonar

Reducing reverberation noise, and increasing resolution drive the ability to detect, and classify underwater mines in a shallow littoral environment. This proposal addresses the problem with an analysis of reverberation noise from recorded sea tests, adaptive beamforming (ABF), and adaptive direction of arrival estimation (ADOA). In the reverberation noise analysis phase, the primary focus will be on the stationarity of the statistics of the noise to determine how quickly the noise statistics change. This will lead to an ABF algorithm requirement that the chosen algorithm adapt as quickly as warranted by the reverberation analysis. Several reduced rank solutions will be tested to determine convergence and array gain. To further improve resolution, several ADOA algorithms will be tested on the real and simulated data. This will lead to high resolution image that can be tested for detection of mine-like features. All this will be performed in software. Timing, and sizing requirements for major software components will be generated in a COTS platform to determine software, and hardware requirements. In the option phase of the proposal, the chosen algorithms will be upgraded to a real time platform.