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

Abstract

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.

RCS Prediction Using Prony Method in High-Frequency Band for Military Aircraft Models

“…The low-frequency scattering analysis approach is capable of producing reliable analysis results; however, the analysis timetends to grow as the frequency increases. The error rate in high-frequency scattering analysis grows in the dB scale rather than the linear scale as compared to low-frequency scattering analysis [4][5][6][7][8][9][10][11][12][13][14][15].…”

“…When the LTI system's function is modeled by imposing the equation at N frequency samples of the data as Equation (12), which is the ratio of a(k) and b(k) polynomials.…”

Scattering of Stealth-material Objects Illuminated by OAM Beams

“…Radar emits electromagnetic waves to illuminate the targets, and receives the reflected electromagnetic waves to detect and image it. With the development of stealth-material (SM) technology, the smaller Radar Cross Section (RCS) of the target make detection of stealthy targets more difficult [1][2][3][4]. At present, radar anti-stealth technology is principally divided into frequency domain anti-stealth, such as ultra-wideband radar (Airspace anti stealth) [5], multi-base radar (polarization domain anti-stealth) [6,7], polarization radar, etc.…”