Approximate Kernel Orthogonalization for Antenna Array Processing

We present a method for kernel antenna array processing using Gaussian kernels as basis functions. The method first identifies the data clusters by using a modified sparse greedy matrix approximation. Then, the algorithm performs model reduction in order to try to reduce the final size of the beamfo...

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation 2010-12, Vol.58 (12), p.3942-3950
Main Authors: Navia-Vazquez, A, Martinez-Ramon, M, Garcia-Munoz, L E, Christodoulou, C G
Format: Article
Language:English
Subjects:
Algorithms
Antenna array processing
Antenna arrays
Antennas
Applied sciences
Approximation methods
Arrays
beamforming
Computational modeling
Computer simulation
Dipoles
DOA
Exact sciences and technology
Hilbert space
kernel method
Kernels
Mathematical models
Radiocommunications
Support vector machines
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Transmission and modulation (techniques and equipments)
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Summary:We present a method for kernel antenna array processing using Gaussian kernels as basis functions. The method first identifies the data clusters by using a modified sparse greedy matrix approximation. Then, the algorithm performs model reduction in order to try to reduce the final size of the beamformer. The method is tested with simulations that include two arrays made of two and seven printed half wavelength thick dipoles, in scenarios with 4 and 5 users coming from different angles of arrival. The antenna parameters are simulated for all DOAs, and include the dipole radiation pattern and the mutual coupling effects of the array. The method is compared with other state-of-the-art nonlinear processing methods, to show that the presented algorithm has near optimal capabilities together with a low computational burden.
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2010.2078458