|Irving S. Reed
University of Southern California
Los Angeles, CA 90089-2564
tel: (213) 740-7335
Abstract Space-Time Adaptive Processing (STAP) has been shown to be very effective in suppressing ground clutter in airborne MTI radars. Practical implementations of STAP are often further enabled by careful attention to rank reducing transformations prior to the STAP processing. A review of a number of different sensor applications reveals that an identical statistical detection framework can arise if the raw sensor data is appropriately preprocessed. We show how multispectral imaging, synthetic aperture radar, and non-coherent processing of infrared images of moving targets all can be transformed into similar STAP problems by very different preprocessing.
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