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基于IRLS的跳频模式下GTD散射参数提取和RCS重构 被引量:1

GTD scattering parameter extraction and RCS reconstruction in frequency hopping mode based on IRLS
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摘要 现代谱估计方法能够反演基于几何绕射理论(geometric theory of diffraction,GTD)的模型参数,但不能处理非均匀不完备的雷达散射截面(radar cross section,RCS)数据。此外,通过暗室测量获取完备的RCS数据也需要较大的时空开销。针对上述问题,提出一种基于迭代加权最小二乘(iteratively reweighed least squares,IRLS)的跳频模式下GTD散射参数提取和RCS重构方法。该方法将稀疏重构理论与GTD散射模型相结合,能够在RCS数据非均匀不完备的条件下反演散射参数和实现RCS重构。仿真数据和电磁计算数据用于验证所提方法的有效性,实验结果表明该方法对降低暗室步进频率RCS的测量成本和扩增雷达RCS数据具有重要意义。 Modern spectral estimation methods can extract the scattering parameters based on the geometric theory of diffraction(GTD)model,but they cannot deal with non-uniform and incomplete radar cross section(RCS)data.Furthermore,obtaining complete RCS data in anechoic chambers requires large space-time cost.To solve these problems,an iteratively reweighed least squares(IRLS)-based method for GTD scattering parameter extraction and RCS reconstruction in frequency hopping mode is proposed.Combining the sparse reconstruction theory with the GTD scattering model,the proposed method can retrieve scattering parameters and realize RCS reconstruction under the condition of non-uniform and incomplete RCS data.Simulation data and electromagnetic calculation data are used to verify the effectiveness of the proposed method.And the experimental results indicate that the proposed method is significant to reduce the cost of stepped-frequency RCS measurement in anechoic chambers and expand RCS data in database.
作者 李英俊 刘永祥 田彪 张文鹏 LI Yingjun;LIU Yongxiang;TIAN Biao;ZHANG Wenpeng(College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China)
出处 《系统工程与电子技术》 EI CSCD 北大核心 2023年第3期678-689,共12页 Systems Engineering and Electronics
基金 国家自然科学基金(61901481,61921001,61901487) 湖南省优秀青年基金(2021JJ20056)资助课题。
关键词 雷达散射截面重构 散射参数提取 几何绕射理论模型 跳频模式 迭代加权最小二乘算法 radar cross section(RCS)reconstruction scattering parameter extraction geometric theory of diffraction(GTD)model frequency hopping mode iteratively reweighed least squares(IRLS)algorithm
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