Unified Approach For Multiscale Radar Cross-Section Measurement by Synthetic Aperture Imaging

Abstract

<div><div>*This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</div></div><div><br></div>In this study, we present an improved and unified approach for image-based radar cross-section (RCS) measurement by 2-D synthetic aperture radar (SAR) imaging with an arbitrary curved antenna scanning trajectory. Because RCS is a quantity defined in the far-field distance of an object under test, direct RCS measurement of an electrically large target is often infeasible owing to the spatial limitation of the measurement facility. The method proposed in this study belongs to the class of techniques referred to as the image-based near-field to far-field transformation (NFFFT) to convert the near-field data of scattering experiment into the far-field RCS. In a previous study, we have developed an NFFFT based on 3-D SAR imaging with an arbitrary antenna scanning surface. However, the previous approach is only applicable to the surface scanning which is impossible for a certain case such as measurement using airborne SAR or vehicle-borne SAR. Therefore, one requires an alternative method that can accommodate an arbitrary scanning curve, which is the subject of this study. We derive a generalized correction factor for image-based NFFFT which is designed to ensure the integral transformation in the image reconstruction process be self-consistent for electrically small scatterers. We provide a series of numerical simulations, an indoor experiment, and an airborne SAR experiment to validate that the proposed scheme can be utilized for various situations ranging from near-field to far-field distance.

Image-Based Radar Cross Section Synthesis for a Cluster of Multiple Static Targets

“…As discussed in [9]- [11], one can prove that the image reconstruction given in Eq. ( 7) reduces to the ideal image defined in Eq.…”

“…In Eq. (8), g(k, r 0 , r) is called the "correction factor" [9]- [11] which is defined such that the integral transformation of Eq. ( 6) is reduced to the ideal image of a single point scatterer given in Eq.…”

“…Note that an arbitrary scanning curve can be used by calculating the derivative in the correction factor of Eq. ( 9) via a numerical differentiation algorithm [10], [11].…”