Material Identification using Reflectivity Kernels

The goal of this project is the discrimination, classification and identification of submerged objects of interest in unfriendly or hostile environments. Our approach to discrimination of targets from clutter and decoys is based on estimating their material invariant reflectivity kernels. We have developed processing algorithms and demonstrated the discrimination of materials from SONAR returns under ideal conditions. Our algorithms are capable of remotely differentiating ensonified objects, be they micromechanically different, chemically different or biologically different. This is a capability which has been lacking in all previous work which we are aware of. The extent of material information in the returned echo depends on the interrogating waveform. We propose to develop a parameterized library of waveforms and associated processing algorithms for material estimation. Waveform parameters will include hardware capabilities and robustness to noise.