Efficient Broadband Monostatic RCS Computation of Morphing S-Shape Cavity Using Artificial Neural Networks

The efficient broadband monostatic radar cross section (RCS) of the electrically large and deep morphing S-shape cavity calculation is a challenge in computational electromagnetic. Moreover, the traditional electromagnetic computing method can be very time-consuming because the highly complex computations of broadband monostatic RCS must be repeated each time the S-shape cavity structure is modified. To tackle these difficulties, artificial neural networks (ANNs) are proposed to reduce the computational cost of conventional methods, and the data-driven ANNs model can be trained with the inputs scheme datasets include three parts, which are the morphing cavity structure parameters, the incident plane wave information, and RCS of the initial morphing cavity. With careful design and proper training, the broadband monostatic RCS of morphing S-shape cavity can be obtained efficiently by ANNs. In the last, three examples are presented to demonstrate the accuracy and efficiency of the proposed method.