Accurate modeling of acoustic propagation in the ocean waveguide is important to SONAR-performance prediction, and requires, particularly in shallow water environments, characterizing the bottom reflection loss with a precision that databank-based modeling cannot achieve. Recent advances in the technology of autonomous underwater vehicles (AUV) make it possible to envision a survey system for seabed characterization composed of a short array mounted on a small AUV. The bottom power reflection coefficient (and the related reflection loss) can be estimated passively by beamforming the naturally occurring marine ambient-noise acoustic field recorded by a vertical line array of hydrophones. However, the reduced array lengths required by small AUV deployment can hinder the process, due to the inherently poor angular resolution. In this dissertation, original data-processing techniques are presented which, by introducing into the processing chain knowledge derived from physics, can improve the performance of short arrays in this particular task. Particularly, the analysis of a model of the ambient-noise spatial coherence function leads to the development of a new proof of the result at the basis of the bottom reflection-loss estimation technique. The proof highlights some shortcomings inherent in the beamforming operation so far used in this technique. A different algorithm is then proposed, which removes the problem achieving improved performance. Furthermore, another technique is presented that uses data from higher frequencies to estimate the noise spatial coherence function at a lower frequency, for sensor spacing values beyond the physical length of the array. By u22synthesizingu22 a longer array, the angular resolution of the bottom-loss estimate can be improved, often making use of data at frequencies above the array design frequency, otherwise not utilized for beamforming. The proposed algorithms are demonstrated both in simulation and on real data acquired during several experimental campaigns.
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