Array beamsteering with side lobe suppression using neural networks trained by Mutated Boolean particle swarm optimized data

Zaharias D. Zaharis, Konstantinos A. Gotsis, John N. Sahalos

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A beamsteering (BS) technique applied on antenna arrays is proposed. The technique is based on neural networks (NNs) and aims at estimating the array excitation weights that produce a main lobe towards every desired signal and achieve low side lobe level (SLL). Initially, the Mutated Boolean particle swarm optimization (MBPSO) is applied to a set of random directions of incoming signals in order to estimate the excitation weights that make a uniform linear array (ULA) produce one or more main lobes towards the respective incoming signals and achieve a SLL equal to or less than a desired value. The estimated weights are then used to train a NN efficiently. The trained NN is applied to a new set of random directions of incoming signals and the derived radiation patterns are compared to respective patterns derived by the MBPSO, a differential evolution based BS technique and the maximum likelihood method. The above comparisons were performed for various SLLs and for one or two desired signals received by a ULA. In the problem, the presence of additive zero-mean Gaussian noise was assumed. The comparative results show the advantages of the proposed BS technique.

Original languageEnglish
Pages (from-to)877-883
Number of pages7
JournalJournal of Electromagnetic Waves and Applications
Volume27
Issue number7
Early online date15 Apr 2013
DOIs
Publication statusPublished - 1 May 2013
Externally publishedYes

Fingerprint Dive into the research topics of 'Array beamsteering with side lobe suppression using neural networks trained by Mutated Boolean particle swarm optimized data'. Together they form a unique fingerprint.

  • Cite this