Abstract
When the target is received in the presence of coherent noise resulting from a large number of complex and randomly distributed scatterers inherent to the medium, the ability of the system to distinguish between the two types of signals often becomes the most crucial performance consideration. Conventional techniques that are capable of suppressing time‐varying (incoherent) noise are generally not effective when the noise is time invariant (coherent). In recent years, diversity techniques have been developed that allow the decorrelation of the coherent noise term by altering either the transmitted frequency or the position of the transducer. Although the diversity techniques provide some noise suppression when used in conjunction with the conventional averaging algorithms, their potential benefits cannot be fully exploited by such linear techniques alone. In the work presented here, a nonlinear detection scheme based on the polarity of the spatially decorrelated signals is examined. The theoretical and experimental results indicate that the polarity‐thresholding algorithm provides target enhancement that is far superior to the linear techniques previously used in spatial processing. Furthermore, the paper examines the spatial decorrelation properties of the experimental data to determine the desirable parameters for data acquisition and signal processing.
Published Version
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