Subspace Averaging for Source Enumeration in Large Arrays

TitleSubspace Averaging for Source Enumeration in Large Arrays
Publication TypeConference Paper
Year of Publication2018
AuthorsSantamaría, I., D. Ramírez, and L. L. Scharf
Conference NameIEEE Statistical Signal Processing Workshop (SSP)
Month PublishedJune
Conference LocationFreiburg, Germany
AbstractSubspace averaging is proposed and examined as a method of enumerating sources in large linear arrays, under conditions of low sample support. The key idea is to exploit shift invariance as a way of extracting many subspaces, which may then be approximated by a single extrinsic average. An automatic order determination rule for this extrinsic average is then the rule for determining the number of sources. Experimental results are presented for cases where the number of array snapshots is roughly half the number of array elements, and sources are well separated with respect to the Rayleigh limit.
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