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- W2140438794 abstract "A new adaptive beamforming technique based on neural networks (NNs) is proposed. The NN training is accomplished by applying a novel optimization method called Mutated Boolean PSO (MBPSO). In the beginning of the procedure, the MBPSO is repeatedly applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. The estimated weights are used to train e-ciently a NN. Finally, the NN is applied to a new set of random cases and the extracted radiation patterns are compared to respective patterns extracted by the MBPSO and a well-known robust adaptive beamforming technique called Minimum Variance Distortionless Response (MVDR). The aforementioned comparison has been performed considering uniform linear antenna arrays receiving several interference signals and a desired one in the presence of additive Gaussian noise. The comparative results show the advantages of the proposed technique." @default.
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- W2140438794 date "2012-01-01" @default.
- W2140438794 modified "2023-10-17" @default.
- W2140438794 title "ADAPTIVE BEAMFORMING WITH LOW SIDE LOBE LEVEL USING NEURAL NETWORKS TRAINED BY MUTATED BOOLEAN PSO" @default.
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- W2140438794 doi "https://doi.org/10.2528/pier12022806" @default.
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