Xiu Tiao Ye

In this article, a novel multibranch encoder-decoder-based artificial neural network (ANN) framework is proposed for pattern-reconfigurable nonuniformly spaced linear array synthesis. In the proposed framework, different encoder–decoder branches are devoted to synthesizing different radiation patterns to satisfy the desired radiation patterns. In each encoder–decoder branch, the encoder, and the decoder serve as the array synthesizer and analyzer, respectively. By minimizing a suitably-defined loss function with respect to multiple radiation patterns, element amplitudes, and positions, not only multiple mask-constrained radiation patterns can be successfully achieved, but also the common element amplitudes and positions with minimum inter-element spacing control can be obtained. Thus, different radiation patterns can be switched by phase-only control. In addition, by using different training samples, the proposed method can consider the ideal synthesis of the array factor, as well as the actual synthesis of antenna arrays to consider the mutual coupling effects in the synthesis process. Simulation results are provided to validate the capability, efficiency, and attractiveness of the proposed method.

The beampattern synthesis of antenna array is always known as a computationally cost task that needs efficient strategies to deal with. In this article, 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 pretrained 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 nonidealities. 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 to the competitive state-of-the-art synthesis techniques.

Due to the range-angle-dependent beampattern, frequency diverse array (FDA), either in the form of linear array or planar array, has been extensively investigated in recent years. However, the conformal FDA is seldom studied. In this paper, a general conformal FDA framework is firstly developed. Specifically, a hemi-spherical FDA is exploited to illustrate the unique advantage of conformal FDA in terms of the three-dimensional localization ability. Furthermore, to deal with the intrinsic defect of time-variant range-angle-coupled beampattern of the FDA, an optimized static nonlinear frequency offset (OSNFO) is proposed. The OSNFO not only decouples the conformal FDA beampattern in the range-angle dimension, but also copes with its inherent time-variant problem, resulting in a quasi-time-invariant three-dimensional focusing beampattern. Numerous simulation results have been provided to demonstrate the superiority and the effectiveness of the OSNFO-conformal FDA.

In this letter, a dividing ratio continuously tunable power divider with a wide tuning range is presented. A pair of tunable shunt stub is utilized to achieve different power dividing ratios and good input matching performance. By varying the loaded capacitance of the tunable stubs, a continuous tuning range from 1:1 to arbitrary dividing ratio can be achieved theoretically. For demonstration, a prototype circuit with the dividing ratio from 1:2 to 1:100 is designed and fabricated. Good agreement can be observed between the measured results and simulated results.