Efficient Neural Network-Based Synthesis of Low Sidelobe Frequency-Invariant Directional Patterns

The synthesis of array antennas is to obtain an antenna array pattern that meticulously fulfills target performance metrics, including the main lobe width, sidelobe height suppression, directivity coefficient enhancement, and the creation of nulls. This intricate design process involves the consideration of factors such as the number of radiating elements, their inter-element spacing, the amplitude excitation ratio, and the excitation phase. This article presents an advancement in the Inverse Fourier Transform (IFT) algorithm, successfully achieving frequency-invariant pattern synthesis for a 32-element, equidistant linear array operating within the X-band, while maintaining sidelobe levels below the stringent threshold of 30dB. Furthermore, to expedite the synthesis process and ensure the generation of low sidelobe, frequency-invariant patterns, an innovative Radial Basis Function neural network (RBFNN) is introduced, harnessing the power of machine learning for rapid and precise pattern optimization.