Advanced Physics-Based Modeling of Discrete Clutter and Diffuse Reverberation in the Littoral Environment

The performance of active Navy sonar systems in littoral environments is often limited by strong reverberation, typically from the seabed. Reverberation can be subdivided into discrete clutter events and diffuse bottom reverberation, with discrete clutterevents being the most serious problem for the utilization of current and future sonar systems. An accurate, physics-based model for the prediction of clutter and diffuse reverberation is sorely needed in order to shape the development of new sonar conceptsand optimize the employment strategies of existing systems. This proposal meets this challenge with an approach that addresses these issues. Charles Holland (ARL/PSU) has designed and conducted innovative measurements to localize and probe the physicalmechanisms controlling seabed clutter and scattering in the littoral. Peter Neumann (PSI) leads a PSI team with experience in the configuration management of models and algorithms as Navy standards. Kevin LePage (NRL-DC) is an expert on modelingreverberation and scattering and participates in this proposal as an outside funded resource. This proposal leverages existing modeling efforts (R-SNAP and BiStaR models) with existing measurement programs (Boundary Characterization and GeoClutter) todevelop a high-fidelity, physics-based model for low to mid-frequency sonars that will accurately simulate both diffuse reverberation and discrete clutter. The anticipated benefit of the proposed project is the reduction in the development costs of newactive Navy sonar systems through the use of the proposed high-fidelity, physics-based model for both diffuse reverberation and discrete clutter. The applicable frequency of the proposed model (from below 100 Hz to 5 KHz) covers a wide range of current andproposed sonar systems. The proposed model leverages the existing, advanced knowledge of diffuse reverberation modeling with the emerging understanding of the mechanisms responsible for discrete clutter into a unified, high-fidelity model.