Adaptive Array Processing for Acoustic Imaging
The need for high resolution acoustic image formation under the constraints imposed by small (≈ 100 wavelengths) apertures, long wavelength radiation, and sparsely sampled discrete apertures is encountered in many applications. Most techniques currently in use require large arrays for resolution and uniform sampling of the array aperture for sidelobe control to yield adequate performance when imaging specular reflectors. This paper examines the applicability of the data-adaptive array processing technique known as the Maximum Likelihood Method to image formation in the undersea environment, where acoustics and acoustic imaging techniques have long been of interest as a result of their utility in probing where electromagnetic radiation will not penetrate. The approach taken is to suppress the usual deterministic outlook wherein the propagation phenomenon is “undone”, and to look at the problem in a statistical sense. The result is an imaging technique that is essentially spatial and temporal spectral density estimation for a space/time random process which is sampled at a small number of discrete spatial locations.
KeywordsSpectral Covariance Acoustic Image Array Processing Steering Vector Reflectance Function
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