*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. In this article, we propose a novel image-based method for radar cross-section (RCS) prediction of a cluster of multiple static targets by synthesizing replicas of an original radar image. In this approach, we first measure the near-field backscattering of a target and reconstruct a corresponding radar image. Then, modified copies of this image with rotation, translation, and spatial filtering, are generated according to the predefined desired arrangement, and they are coherently summed to create a single synthesized image in which all scattering contributions contained in the modified images are virtually included. Finally, the synthesized image is utilized to predict the far-field RCS of the multiple targets, based on the theory of image-based near-field-to-far-field transformation (NFFFT). By employing the proposed algorithm, we can avoid building multiple test targets, resulting in the reduction of the production costs of them. Moreover, we can easily test several different experimental layouts of the multiple targets without repeating a real measurement. Numerical simulations and experiments are provided to demonstrate the validity of the proposed image-based RCS synthesis.

*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. This article is intended to provide practical information about development of a cost-effective test model to achieve low-cost radar cross-section (RCS) measurement. To reduce the manufacturing cost of a model, two major possible approaches are: replacing the molding material with inexpensive one, and measuring a scaled-down model with a higher operating frequency. In this study, the former was achieved by using a resin-made model coated with conductive paste to imitate a complete metallic model. According to the scaling laws, the entire geometry, including the surface roughness of the model owing to the conductive paint, must be properly scaled down. As this roughness scaling is practically infeasible, we experimentally prove that the shape-only scaling is valid without the roughness scaling, provided that appropriate molding material is selected. As a preliminary study, two types of polyurethane resin with different density were tested for their suitability of the RCS measurement, and the resin with higher density was shown to be appropriate for the RCS test model. Based on this preliminary study, 1/20- and 1/40-scale aircraft models made with aluminum or polyurethane resin were manufactured, and their RCS were measured to demonstrate that the almost equivalent RCS patterns can be obtained; therefore, low-cost RCS testing was achieved by the resin-made scaled model.

*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. 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.

This article has been accepted for publication in IEEE Transactions on Antennas and Propagation. Citation information: T. Watanabe and H. Yamada, “Far-Field Radar Cross-Section Determination From Near-Field 3-D Synthetic Aperture Imaging With Arbitrary Antenna-Scanning Surfaces,” IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2022.3161491. © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this study, we propose a generalized algorithm for far-field radar cross-section determination by using 3-D synthetic aperture imaging with arbitrary antenna-scanning surfaces. This method belongs to a class of techniques called image-based near-field-to-far-field transformation. The previous image-based approaches have been formulated based on a specific antenna-scanning trajectory or surface, such as a line, plane, circle, cylinder, and sphere; majority of these approaches consider 2-D radar images to determine the azimuth radar cross-section. We generalize the conventional image-based technique to accommodate an arbitrary antenna-scanning surface and consider a 3-D radar image for radar cross-section prediction in both the azimuth and zenith directions. We validate the proposed algorithm by performing numerical simulations and anechoic chamber measurements.

In this research, we discuss the possibility of incorrect prediction of the double-bounce scattering (DBS) power in model-based decomposition (MBD) algorithms applied to polarimetric synthetic aperture radar (SAR) images of vegetated terrain. In most of the MBD schemes, the estimation of the DBS component is based on the assumption that the co-polarized phase difference (CPD) of the DBS is similar to those of backscattering from a pair of orthogonal planer conducting surfaces. However, for dielectric surfaces such as soil or vegetation trunks, this assumption is only valid within a certain range of radar incidence angle, which is dictated by the Brewster angles of the dielectric surfaces. If the incidence angle is out of this range, the DBS contribution is incorrectly estimated as the surface scattering. Moreover, because the Brewster angle is a function of surface permittivity, the angular range depends on moisture contents of the surfaces; therefore, correctness of the MBD results also depend on the surface moisture contents. To demonstrate this problem, we provide a simple numerical model of vegetated terrain, and we validate theoretical results by a series of controlled experiments carried out in an anechoic chamber with a simplified vegetation model.

*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. In this article, we propose a 3-D synthetic aperture imaging method with spherical antenna scanning to identify scatterers located close to an antenna array, such as fixtures or support of the antenna. Previous studies have shown that 2-D and 3-D synthetic aperture imaging techniques with planar, circular, and cylindrical scanning can successfully reconstruct spatial images of near-field scatterers. The spherical scanning approach considered in this article is expected to improve the 3-D image resolution because more angular diversity can be achieved in the elevation direction. However, as we show in this study, simple extension of the previous techniques to the spherical case results in undesired blur artifacts in the reconstructed image. To overcome this problem, we introduce a correction factor in the image reconstruction. The proposed imaging algorithm is validated by numerical electromagnetic simulation based on the method of moments.