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

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

doi:10.2528/pier12022806

Cited by 33 publications

(28 citation statements)

References 39 publications

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“…For that reason, an alternative approach based on artificial neural networks (ANNs) has been proposed in this paper. Neural networks are very convenient as a modeling tool since they have the ability to learn from presented data [17][18][19][20][21]. Compared to conventional signal processing algorithms that are mainly based on linear models, neural networks consider DOA estimation as approximation of highly nonlinear multidimensional function, or in other words, as a mapping between spatial covariance matrix of received signals from antenna elements and DOAs.…”

Section: Introductionmentioning

confidence: 99%

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Agatonovic¹,

Stankovic²,

Milovanović³

et al. 2013

PIER

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Abstract-In this paper, we present an efficient Artificial Neural Network (ANN)-based model to estimate both azimuth and elevation arrival angles of a signal source. To achieve this goal, the ANN model is constructed using measurement data obtained by a rectangular antenna array in the space-frequency domain. Unlike classical superresolution algorithms such as 2D MUSIC, the proposed model is capable to account for imperfections of measurement equipment as well as mutual couplings between array elements. The neural model has been verified for several angular positions and frequencies. It is shown that the use of ANN model to estimate angular positions of a signal source yields more accurate results when compared to 2D MUSIC. Moreover, the neural model significantly outperforms 2D MUSIC in terms of speed of computation.

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Section: Introductionmentioning

confidence: 99%

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Agatonovic¹,

Stankovic²,

Milovanović³

et al. 2013

PIER

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“…Examples of this can be found in [15], where an adaptive Neural Network (NN) beamformer is trained using a Mutated Boolean PSO (MBPSO). The beamformer is compared favorably to the Minimum Variance Distortionless Response (MVDR) beamformer.…”

Section: Pso Used In Radarmentioning

confidence: 99%

Radar Angle of Arrival System Design Optimization Using a Genetic Algorithm

2017

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An approach for using a Genetic Algorithm (GA) to select radar design parameters related to beamforming and angle of arrival estimation is presented in this article. This was accomplished by first developing a simulator that could evaluate the localization performance with a given set of design parameters. The simulator output was utilized as part of the GA objective function that searched the solution space for an optimal set of design parameters. Using this approach, the authors were able to more than halve the mean squared error in degrees of the localization algorithm versus a radar design using human-selected design parameters. The results of this study indicate that this kind of approach can be used to aid in the development of an actual radar design.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Robust Superdirective Beamforming for Hf Circular Receive Antenna Arrays

Zhou¹,

Gao²,

Zhang³

et al. 2013

PIER

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Abstract-Superdirective beamforming can highly reduce the aperture size of high-frequency receive array. At the same time, the closely spaced elements of a small aperture array can make it low efficiency and sensitivity to the array uncertainty, which limit its application in practice. Using a parameter called sensitivity factor, we found that array efficiency and robustness against array error could be considered simultaneously. On that basis, we derive a novel superdirective beamforming criterion based on a constrained sensitivity factor for the HF circular receive array. New method is analytical and computationally inexpensive. Through making the directive gain with a given sensitivity factor maximum, we calculate the optimal weights of the array elements. To illustrate the proposed method can increase the acceptance of HF superdirective receive arrays in practice, several numerical results are provided.

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

Cited by 33 publications

References 39 publications

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Radar Angle of Arrival System Design Optimization Using a Genetic Algorithm

Robust Superdirective Beamforming for Hf Circular Receive Antenna Arrays

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