Overview of an image-based technique for predicting far-field radar cross section from near-field measurements

2003 ◽  
Vol 45 (6) ◽  
pp. 159-169 ◽  
Author(s):  
I.J. LaHaie
Keyword(s):  
Cross Section ◽  
Near Field ◽  
Field Measurements ◽  
Radar Cross Section ◽  
Far Field

scholarly journals Far-Field Radar Cross-Section Determination From Near-Field 3-D Synthetic Aperture Imaging With Arbitrary Antenna Scanning Surfaces

2021 ◽  
Author(s):  
Takuma Watanabe ◽  
Hiroyoshi Yamada
Keyword(s):  
Cross Section ◽  
Near Field ◽  
Radar Cross Section ◽  
Synthetic Aperture ◽  
Far Field ◽  
Synthetic Aperture Imaging ◽  
Radar Images ◽  
Field Transformation ◽  
Chamber Measurements ◽  
Previous Image

In this study, we propose a generalized algorithm for far-field radar cross-section determination from 3-D synthetic aperture imaging with arbitrary antenna scanning surfaces. This method belongs to the class of techniques called image-based near-field to far-field transformation. The previous image-based approaches were formulated based on a specific antenna scanning surface or trajectory such as a line, a plane, a circle, a cylinder, and a sphere--and the majority of them considered 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 demonstrate the proposed algorithm via numerical simulations and anechoic chamber measurements.

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

2021 ◽  
Author(s):  
Takuma Watanabe ◽  
Hiroyoshi Yamada
Keyword(s):  
Cross Section ◽  
Integral Transformation ◽  
Near Field ◽  
Radar Cross Section ◽  
Synthetic Aperture ◽  
Far Field ◽  
Unified Approach ◽  
Reconstruction Process ◽  
Sar Imaging ◽  
Airborne Sar

<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.

scholarly journals Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6023
Author(s):  
Yang Liu ◽  
Weidong Hu ◽  
Wenlong Zhang ◽  
Jianhang Sun ◽  
Baige Xing ◽  
...  
Keyword(s):  
Neural Network ◽  
Cross Section ◽  
Field Data ◽  
Prior Information ◽  
Near Field ◽  
Radar Cross Section ◽  
Acquisition Time ◽  
Far Field ◽  
Regression Estimator ◽  
Regression Problem

Radar cross section near-field to far-field transformation (NFFFT) is a well-established methodology. Due to the testing range constraints, the measured data are mostly near-field. Existing methods employ electromagnetic theory to transform near-field data into the far-field radar cross section, which is time-consuming in data processing. This paper proposes a flexible framework, named Neural Networks Near-Field to Far-Filed Transformation (NN-NFFFT). Unlike the conventional fixed-parameter model, the near-field RCS to far-field RCS transformation process is viewed as a nonlinear regression problem that can be solved by our fast and flexible neural network. The framework includes three stages: Near-Field and Far-field dataset generation, regression estimator training, and far-field data prediction. In our framework, the Radar cross section prior information is incorporated in the Near-Field and Far-field dataset generated by a group of point-scattering targets. A lightweight neural network is then used as a regression estimator to predict the far-field RCS from the near-field RCS observation. For the target with a small RCS, the proposed method also has less data acquisition time. Numerical examples and extensive experiments demonstrate that the proposed method can take less processing time to achieve comparable accuracy. Besides, the proposed framework can employ prior information about the real scenario to improve performance further.

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