Comparative Study of Neural Network Training Applied to Adaptive Beamforming of Antenna Arrays

Abstract

Abstract-This paper presents a comparative study of neural network (NN) training. The trained NNs are used as adaptive beamformers of antenna arrays. The training is performed either by a recently developed method called Mutated Boolean PSO (MBPSO) or by a well known beamforming method called Minimum Variance Distortionless Response (MVDR). The training procedure starts by applying the MBPSO and the MVDR to a set of random cases where a linear antenna array receives a signal of interest (SOI) and several interference signals at random directions of arrival (DOA) different from each other in the presence of additive Gaussian noise. For each case, the MBPSO and the MVDR are independently applied to estimate respective excitation weights that make the array steer the main lobe towards the DOA of the SOI and form nulls towards the DOA of the interference signals. The lowest possible value of side lobe level (SLL) is additionally required. The weights extracted by the MBPSO and the weights extracted by the MVDR are used to train respectively two different NNs. Then, the two trained NNs are independently applied to a new set of cases, where random DOA are chosen for the SOI and the interference signals. Finally, the radiation patterns extracted by the two NNs are compared to each other regarding the steering ability of the main lobe and the nulls as well as the side lobe level. The comparison exhibits the superiority of the NN trained by the MBPSO.

Robust Superdirective Beamforming for Hf Circular Receive Antenna Arrays

“…Array signal processing has been widely used in sensing and dataacquisition systems ranging from radar [1], mobile communications [2][3][4][5], cognitive radio [6] and medical imaging [7]. A versatile approach of array signal processing is beamforming [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], which is used to detect and enhance a desired signal while suppressing interference and noise at the output of an array of sensors.…”

“…A versatile approach of array signal processing is beamforming [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], which is used to detect and enhance a desired signal while suppressing interference and noise at the output of an array of sensors. It is well known that the benefits brought by beamforming such as signal-to-noise ratio enhancement and spatial resolution are restricted by the ratio of array aperture size relative to the working wavelength.…”

A Dual-Frequency Method of Eliminating Liquid Water Radiation to Remotely Sense Cloudy Atmosphere by Ground-Based Microwave Radiometer

“…Radiometer profiling during dynamic weather conditions is discussed and shows that the accuracy of radiometer retrievals is similar to radiosonde soundings when used for numerical weather prediction [19]. The method of artificial neural network is used in various fields [20][21][22][23][24]. Artificial neural network is increasingly used for its more accurate estimation of the atmospheric parameters in the case of strong nonlinearities [25][26][27][28][29][30][31][32].…”

Efficient Implementation of the Capon Beamforming Using the Levenberg-Marquardt Scheme for Two Dimensional Aoa Estimation

“…Angleof-arrival (AOA) estimation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] and beamforming [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] have been main research topics of the array signal processing. Note that array signal processing can also be applied to radar signal processing [39][40][41][42][43].…”