Research On The Analysis Method Of Electromagnetic Scattering Characteristics Of Targets With Uncertain Shape Of TVU

With the rapid development of social demand,the accurate and efficient electromagnetic simulation analysis methods for different practical situations are always the focus of the computational electromagnetics research.In practical engineering applications,the geometrical shape of targets is uncertainty due to the human or environmental factors,the electromagnetic scattering characteristics of targets will be changed accordingly.According to the precise and high frequency approximation method,several uncertainty problems analyzing methods are proposed in this paper.For the electromagnetic scattering analysis from electrically large bodies of revolution(Bo R)with uncertainty geometrical shapes,the uncertainty geometrical shape analyzing methods based on Bo R-based EFIE,EPA-Bo R and Bo R-based PMCHWT theory are proposed.These methods started by describing the geometrical shape of the body of revolution with the two-dimensional non-uniform rational B-spline curves modeling method.Then the relationship can be constructed between the local random variables and the spatial Bo R basis functions.Thus,the surface integral equations were represented in terms of independent random variables.Scattering characteristics can be quickly obtained for perturbed geometrical shapes.For the electromagnetic scattering analysis from electrically large general target with uncertainty geometrical shapes,we fabricated an effective target recognition network-based on the convolutional neural network and the perturbation Shooting and Bouncing Rays(SBR)algorithm for electrically large targets having varying geometrical shapes.The algorithm started by constructing a relationship between the local random variables and target's geometry by using the non-uniform rational B-spline surface modeling method.Inverse synthetic aperture radar(ISAR)imaging was performed quickly for perturbed geometrical shapes and the algorithm can achieve higher computational efficiency than the Monte Carlo method.The ISAR imaging database was built,and the database can describe the local geometrical uncertainty of the target.All the ISAR imaging results were used to train the CNN-based training network.When an ISAR image of target with uncertainty shape was inputted,this target could be recognized via the proposed CNN-based target recognition network.