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RCS Prediction Method from One-Dimensional Intensity Data in Near-Field

机译:基于近场一维强度数据的RCS预测方法

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Radar Cross Section (RCS) can be obtained from near-field data by using near-field to far-field RCS transformation methods. Phase errors in near-field data cause the degradation of the prediction accuracy. In order to overcome the difficulty, we propose the far-field RCS prediction method from one-dimensional intensity data in near-field. The proposed method is derived by extending the phase retrieval method based on the Gerchberg-Saxton algorithm with the use of the relational expression between near-fields and scattering coefficients. The far-field RCS can be predicted from the intensity data of scattered fields measured at two different ranges. The far-field RCS predicted by the proposed method approximately coincides with the computed one. The proposed method also has significant advantages of simple and efficient algorithm. The proposed method is valuable from a practical point of view.
机译:可以通过使用近场到远场RCS转换方法从近场数据获得雷达横截面(RCS)。近场数据中的相位误差会导致预测精度下降。为了克服这一困难,我们提出了一种基于近场一维强度数据的远场RCS预测方法。该方法是通过利用近场与散射系数之间的关系表达式扩展基于Gerchberg-Saxton算法的相位检索方法而得出的。可以根据在两个不同范围内测得的散射场的强度数据来预测远场RCS。通过所提出的方法预测的远场RCS与计算的近似。所提出的方法还具有简单有效的算法的显着优点。从实用的角度来看,所提出的方法是有价值的。

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