Radar Cross-Section Prediction of Electrically Large Target and Its Components by Using Scaling Model and Near-Field to Far-Field Transformation Based on Radar Imaging

To understand detectability of a radar target such as a vehicle, vessel, aircraft, etc., it is important to obtain a radar cross-section (RCS) of the whole target and its components. For an electrically large target whose dimension is several hundred times the wavelength, the measurement distance satisfying the far-field (FF) condition cannot be obtained. Scaling model measurement is one approach to satisfy the FF condition in an anechoic chamber. However, the FF condition cannot be satisfied even in the scaling model for the too large target. The image-based near-field to far-field transformation (IB-NFFFT) can predict the RCS of the large scaling model from the measured near-field (NF) samples, and in addition, this method can also predict the RCS of the components. It makes us possible to discuss and improve accuracy of the RCS prediction through component analysis and filtering. This paper experimentally presents the RCS prediction of the large target and its components by using the scaling model and the IB-NFFFT. The size of the scaling model in this paper is 472 times the wavelength at 50 GHz frequency. The IB-NFFFT can predict the RCS pattern of the electrically large scaling model, obtain the inverse synthetic aperture radar (ISAR) image of the model, and briefly predict the RCS of the components of the model. This paper finds out the ability of the IB-NFFFT to obtain the RCS of the electrically large target and its components in the anechoic chamber. The finding is important because the large target has its unique features such as the rapid fluctuation of the RCS pattern and a lot of components contributing to the RCS.