Scattering Prediction from Data of Scale Model Based on Regression Method in SPSS

摘要

Radar cross-section (RCS) measurement of large targets has always been an important research direction in the field of electromagnetism. The measurement of full-scale targets is expensive, and it is difficult to meet the test conditions. This problem can be solved theoretically by the similarity principle of electromagnetism. The RCS measurement of large targets using a scale model is the main method studied in this thesis. The article first analyzes the thin cylindrical model of a perfect conductor and a polytetrafluoroethylene (PTFE) dielectric. The data is obtained by FEKO software, and is simply processed and imported into SPSS for regression analysis to fit the formula. The relationship of the RCS between the original model and the scaled model under different scaling factors is analyzed. The formula of regression fitting and the formula derived by theory are compared to verify the effectiveness of the method. Finally, the same method is applied to the perfect conductor with absorbing material on the surface. By comparing the relative error between the formula and the original data, the applicability of this method is further verified and expanded.