Design of Sum and Difference Patterns With Common Nulls and Low SLLs Simultaneously in the Presence of Array Errors

Xu, Yanhong; Shi, Xiaowei; Wang, Anyi; Xu, Jingwei

doi:10.1109/tap.2018.2880100

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…The array imperfections typically contain the elements position error, the excitation error of the elements, and the mutual coupling between the elements. All these imperfections can be simplified as the inconsistence of the steering vectors [15]:…”

Section: Calculation Of Array Imperfectionsmentioning

confidence: 99%

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Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

Sallam

Attiya

2021

Int. J. Microw. Wireless Technol.

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Achieving robust and fast two-dimensional adaptive beamforming of phased array antennas is a challenging problem due to its high-computational complexity. To address this problem, a deep-learning-based beamforming method is presented in this paper. In particular, the optimum weight vector is computed by modeling the problem as a convolutional neural network (CNN), which is trained with I/O pairs obtained from the optimum Wiener solution. In order to exhibit the robustness of the new technique, it is applied on an 8 × 8 phased array antenna and compared with a shallow (non-deep) neural network namely, radial basis function neural network. The results reveal that the CNN leads to nearly optimal Wiener weights even in the presence of array imperfections.

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Section: Calculation Of Array Imperfectionsmentioning

confidence: 99%

“…The optimum design of the antenna array is difficult to accomplish because of the array imperfections, such as the amplitude and phase errors, the effect of mutual coupling, etc. [14,15]. All these uncertainties of the array would result in the increase of the null and sidelobe levels.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

Sallam

Attiya

2021

Int. J. Microw. Wireless Technol.

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show abstract

“…The array imperfections typically contain the elements position error, the excitation error of the elements and the mutual coupling between the elements. All these imperfections can be simplified as the inconsistence of the steering vectors [21]:…”

Section: Calculation Of the Array Imperfectionsmentioning

confidence: 99%

“…Besides, the optimum design of the antenna array is difficult to accomplish because of the array imperfections, such as the amplitude and phase errors, the effect of mutual coupling, etc [20], [21]. All these uncertainties of the array would result in the increase of the null and sidelobe levels, what's more, it will shift the nulling directions.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Phase-Only Nulling Based on Deep Neural Network With Robustness

Zhao

Tang

2019

IEEE Access

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Phase-only nulling under sidelobe and mainlobe constraints is a problem of interest in array synthesis which is a nonlinear problem without analytical solution. To reduce the computational cost of phase-only array nulling on-line, this paper proposes a real-time phase-only array synthesis method based on the deep neural network. The on-line real-time prediction of element excitation phase is achieved by the trained neural network which can be done off-line. The performance of the trained neural network is related with the number of data. Firstly, in order to obtain a large enough database for the deep neural network efficiently, a multi-task phase-only array synthesis model with nulling operation and sidelobe control is relaxed to a convex problem and solved by direct iterative rank refinement. Then, the deep neural network is devised to emulate the phase array nulling behavior. This is carried out by the design of the structure of the network, the dataset structure and the loss function of the network. To validate the performance of the deep neural network, the phase-only nulling of 10-element and 16-element linear array based on the deep neural network is realized and tested. Experimental results demonstrate that the proposed real-time array synthesis method not only satisfies the desired array pattern property but also shows robustness to the array imperfections. Robustness is validated with Monte Carlo test.INDEX TERMS Array pattern synthesis, deep neural network, interference nulling, robust array synthesis.

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“…Under this circumstance, we aim to propose an efficient low-PSLL beampattern synthesis approach in the presence of array errors based on this algorithm to utilize its excellent performance. A more general array error model established in [24] is adopted during the low-PSLL optimization procedure, where the above mentioned errors are boiled down to the amplitude and phase response errors. Meanwhile, the covariance matrix of the sidelobe region is incorporated into the optimization problem.…”

Section: Introductionmentioning

confidence: 99%