Design of a novel antenna array beamformer using neural networks trained by modified adaptive dispersion invasive weed optimization based data

A new antenna array beamformer based on neural
networks (NNs) is presented. The NN training is performed by
using optimized data sets extracted by a novel invasive weed
optimization (IWO) variant called modified adaptive dispersion
IWO (MADIWO). The trained NN is utilized as an adaptive
beamformer that makes a uniform linear antenna array steer
the main lobe toward a desired signal, place respective nulls
toward several interference signals, and suppress the side lobe
level (SLL). Initially, the NN structure is selected by training
several NNs of various structures using MADIWO-based data
and by making a comparison among the NNs in terms of training
performance. The selected NN structure is then used to construct
an adaptive beamformer, which is compared to MADIWO-based
and ADIWO-based beamformers, regarding the SLL and the
ability to properly steer the main lobe and the nulls. The comparison
is made, considering several sets of random cases with
different numbers of interference signals and different power
levels of additive zero-mean Gaussian noise. The comparative
results exhibit the advantages of the proposed beamformer.