Robust adaptive nonlinear beamforming by kernels and projection mappings

Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada

Research output: Contribution to journalConference articlepeer-review


This paper introduces a novel adaptive nonlinear beam-forming design by using the wide frame of Reproducing Kernel Hilbert Spaces (RKHS). The task is cast in the framework of convex optimization. A collection of closed convex con-straints is developed that describe: (a) the information dic-tated by the training data and, (b) the required robustness against steering vector errors. Since a time recursive solution is sought, the task is equivalent with the problem of finding a point, in a Hilbert space, that satisfies an infinite number of closed convex constraints. An algorithm is derived using projection mappings. Numerical results show the increased resolution offered by the proposed approach, even with a few antenna elements, as opposed to the classical Linearly Constrained Minimum Variance (LCMV) beam-former, and to a nonlinear regression approach realized by the Kernel Recur-sive Least Squares (KRLS) method. copyright by EURASIP.

Original languageEnglish (US)
JournalEuropean Signal Processing Conference
StatePublished - Dec 1 2008
Event16th European Signal Processing Conference, EUSIPCO 2008 - Lausanne, Switzerland
Duration: Aug 25 2008Aug 29 2008


Dive into the research topics of 'Robust adaptive nonlinear beamforming by kernels and projection mappings'. Together they form a unique fingerprint.

Cite this