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Post-STAP Processing
Detection and Track with
Multidimensional Data
Maps and the Hough

Blair D. Carlson
MIT Lincoln Laboratory
244 Wood Street
Lexington, MA 02173-9108
email: bcarlson@ll.mit.edu

A method of viewing search radar signals and data is described in which the image processing technique of the Hough transform is used to extract detections and simultaneous tracks from multidimensional data maps. The Hough transform is a feature detector that is used to detect lines or curves in images or data maps. This type of "data radar" has applicability as a post-STAP detector that still may have to deal with residual clutter false alarms. In essence, the Hough radar is a nonchoerent integrator that avoids the traditional problems of long-term integration such as range walk, Doppler walk, and beam crossover. It does this by integrating all of the energy from a target along its trajectory through a multidimensional data space and not just its projection into cells on a facet of the space as a traditional radar would. This type of multidimensional data radar exhibits improved performance by making better use of old data that may not have been strong enough to create a detection in a traditional radar and would have been thrown out. This type of radar also facilitates the use of data from multiple, possible dissimilar and possibly moving sensors in a combined detection of data fusion. The capability of a data radar would grow as computer speeds and memory sizes grow. Computer visualization and image processing advancements could easily be incorporated without great changes to the RF sensor portion of the hardware. Simulations and examples from real data sets from several radars including the RSTER Mountaintop are presented.



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