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Underdetermined LS Algorithms for Adaptive 2D . . .

By George-othon Glentis and Kristina Georgoulakis

Abstract

In this paper, Underdetermined Least Squares algorithms are derived, for Two-Dimensional adaptive linear ltering and prediction. The derivation of the proposed algorithms is based on the spatial shift invariance properties that the 2D discrete time signals possess. The proposed algorithms have low computational complexity. The convergence speed and the tracking ability of the proposed schemes, is comparable to that of that of the higher complexity 2D RLS algorithms. The performance of the proposed algorithms is illustrated by simulation

Topics: Two-Dimensional Adaptive Filtering, Least-Squares Algorithms
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.417.1317
Provided by: CiteSeerX
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