Evaluation of a Neural-Network-Based Adaptive Beamforming Scheme With Magnitude-Only Constraints

Castaldi, G.; Galdi, Vincenzo; Gerini, G.

doi:10.2528/pierb08092303

Cited by 18 publications

(14 citation statements)

References 24 publications

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“…Our study presents a new adaptive beamforming (ABF) technique suitable for antenna arrays [3][4][5][6][9][10][11][12][13][14][15][16][17]. The technique is based on neural networks (NNs) [5,[18][19][20][21][22][23][24][25][26], which use training sets produced by a novel binary variant of Particle Swarm Optimization (PSO) [27][28][29][30][31][32][33], called Mutated Boolean PSO (MBPSO) [10].…”

Section: Introductionmentioning

confidence: 99%

“…Taking into account that the geometry of the array is time-independent, a dynamically shaped pattern is achieved by applying on the array elements appropriate excitation weights that vary with time. These weights are calculated in real time by beamforming techniques [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Therefore, the beamforming algorithm must be completed as fast as possible.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Beamforming With Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean Pso

Zaharis

Gotsis

Sahalos

2012

PIER

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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 efficiently 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Beamforming With Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean Pso

Zaharis

Gotsis

Sahalos

2012

PIER

View full text Add to dashboard Cite

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“…ANN is one of the most popular and widely applied methods due to its superior ability of approximating unknown nonlinear function to any degree of desired accuracy. This method has been widely applied to many fields, such as image processing, pattern recognition, signal processing, and weather prediction, particularly in the area of the electromagnetics [24-26, [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method of Obtaining Degrees of Freedom for Radial Airgap Length in SRM Under Normal and Faulty Conditions Based on Magnetiostatic Model

Torkaman¹,

Afjei²

2010

PIER

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Abstract-In this paper, a new hybrid method of obtaining the degrees of freedom for redial airgap length in Switched Reluctance Motor operation under normal and faulty conditions based on magnetostatic analysis is presented. At the beginning, this method goes through the magnetic design of the motor utilizing three dimensional (3-D) Finite Element Method (FEM) in order to consider the end effects as well as axial fringing field effects. The motor parameters, such as torque, flux linkage, flux density versus rotor position are precisely obtained. Then, a Multi Layered Perception Neural Network is designed by considering the nonlinear behavior of the motor parameters obtained under different modes of operation. Using this network and the obtained parameters from FEM, an Objective Function (OF) for torque ripple with the aim of having a minimum mean square error is estimated. In addition, an improved Genetic Algorithm (GA) for the minimization the OF is also presented to determine the motor's operational regions. Finally, the legal intervals for different modes of motor operation are addressed.

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“…These signals come from different directions, and the space filtering techniques (beamforming) can be used to suppress interference partially [17][18][19][20]. It is assumed that the antenna is just an L-length uniform linear array with digital beamforming capability.…”

Section: Received Signal Model Of Shared-spectrum Multistatic Radarmentioning

confidence: 99%

Co-Frequency Interference Suppression Algorithm via Maximum Signal Minus Interference Level

Kong¹,

Luo²

2010

PIER

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Abstract-Co-frequency interference problem is severe in sharedspectrum multistatic radar system, leading to detection problems in applications. In order to mitigate the co-frequency interferences, an adaptive pulse compression algorithm based on Maximum Signal Minus Interference Level criterion (MSMIL) is proposed in this paper, the main idea of which is to adaptively design an appropriate filter at each range cell, maximizing the result of signal power minus interference power, so that both range sidelobes and cross-correlation interference can be suppressed. Simulation results show that the algorithm outperforms the traditional multistatic adaptive pulse compression (MAPC) method in interference suppression. Compared with MAPC method, after one iteration step, the output SIR of the proposed algorithm is increased by about 10 dB. And after two iteration steps, it is increased by more than 40 dB. Moreover, it also outperforms the MAPC in convergence speed.

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Evaluation of a Neural-Network-Based Adaptive Beamforming Scheme With Magnitude-Only Constraints

Cited by 18 publications

References 24 publications

Adaptive Beamforming With Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean Pso

Adaptive Beamforming With Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean Pso

Hybrid Method of Obtaining Degrees of Freedom for Radial Airgap Length in SRM Under Normal and Faulty Conditions Based on Magnetiostatic Model

Co-Frequency Interference Suppression Algorithm via Maximum Signal Minus Interference Level

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