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“Subspace-Based Direction Finding Methods”
by Egemen Gönen and Jerry M. Mendel
November 1996
The direction finding problem is to estimate the bearings [i.e., directions of arrival (DOA)] of a collection of sources from an array of measurements made at a collection of time points. In applications, the Rayleigh criterion sets a bound on the resolution power of classical direction finding methods. In this report we summarize many of the so-called super-resolution subspace-based direction finding methods which may overcome the Rayleigh bound. We divide these methods into two classes, those that use second-order statistics, and those that use second- and higher-order statistics.
Not only do we describe all of the popular subspace-based algorithms, but we also provide flowcharts and figures that guide a reader to understand when to use a second-order or a higher-order statistics based method. Using these flowcharts and figures, it is possible for a potential user of a subspace-based direction finding method to decide which method(s) is (are) most likely to give best results for his/her application.
This report is a chapter that will appear in the Digital Signal Processing Handbook, that will be published by CRC Press in 1997 or 1998.