Prof. Wen Tao Li

Xidian University, China

Paper Title: C. Cui, W. T. Li, X. T. Ye, P. Rocca, Y. Q. Hei and X. W. Shi, ``An Effective Artificial Neural Network-Based Method for Linear Array Beampattern Synthesis,`` IEEE Trans. Antennas Propag., vol. 69, no. 10, pp. 6431-6443, Oct. 2021.

Abstract: The beampattern synthesis of antenna array is always known as a computationally-cost task that needs efficient strategies to deal with. In this paper, a novel artificial neural network (ANN)-based array synthesis method is proposed. By establishing an encoder-decoder-based ANN framework, the mask-constrained beampattern in terms of focused or shaped beam for linear arrays with arbitrary given array geometry is successfully synthesized. More in detail, the encoder serves as an array synthesizer, while the decoder behaves as an array analyzer. Thanks to the designed pre-trained decoder, such an approach is computationally efficient so that real-time array synthesis can be potentially achieved. Moreover, the proposed method allows one to consider both ideal and actual linear arrays with mutual coupling effects and non-idealities. The results of a wide numerical validation involving amplitude-only, phase-only, and amplitude-phase excitation syntheses are presented to assess the flexibility and the versatility of the proposed method also in comparison with competitive state-of-the-art synthesis techniques.

Time: 16:00-17:00 (Singapore Time) 25 October 2022 (Tuesday)

Venue: Online (Zoom). Click here to register.