基于快速偶极子法求解导体目标RCS

发布时间：2018-01-10 04:18

本文关键词：基于快速偶极子法求解导体目标RCS　出处：《安徽大学》2017年硕士论文　论文类型：学位论文

【摘要】：目标电磁散射特性的分析是计算电磁学领域的重要研究方向之一,在地质勘测、雷达探测、目标识别等领域得到了广泛应用。对一些电大尺寸或结构复杂目标的电磁散射特性进行精确、高效的分析一直是计算电磁学领域的研究热点之一。矩量法具有精度高、高效性的特点,是分析目标电磁散射特性的数值方法之一。但是传统矩量法在分析电大尺寸目标时,所需的计算时间比较长,而且效率较低。为了解决这一问题,本文提出了两种快速计算目标雷达散射截面(RCS)的方法。第一种方法是将快速偶极子法(FDM)结合再压缩自适应交叉近似(RACA)算法应用于导体目标RCS的计算。FDM有利于实现远场组矩阵向量积的快速计算。为了进一步加快近场组互阻抗元素的填充,采用RACA算法对阻抗矩阵进行进一步压缩。与传统FDM相比,该方法计算时间和内存消耗得到了有效缩减。第二种方法是基于特征基函数法(CBFM),将FDM与CBFM-II相结合,加速次要特征基函数和缩减矩阵构造中远场组间的矩阵向量乘积的速度。将ACA算法与之结合进一步加快近场组阻抗矩阵的填充,提高了计算效率和节约了内存。
[Abstract]:The analysis of electromagnetic scattering characteristics of target is one of the important research directions in the field of computational electromagnetics, which is used in geological survey and radar detection. Target recognition and other fields have been widely used. The electromagnetic scattering characteristics of some electrically large or complex targets are accurate. Efficient analysis has always been one of the hotspots in the field of computational electromagnetics. The method of moments has the characteristics of high accuracy and high efficiency. It is one of the numerical methods to analyze the electromagnetic scattering characteristics of the target. But the traditional method of moments takes a long time and low efficiency to analyze the electrically large size target. In order to solve this problem. In this paper, two methods for fast calculating radar cross section (RCS) of target are presented. The first method is to combine the fast dipole method with the recompression adaptive cross approximation (RACA). The algorithm is applied to the calculation of conductor target RCS. The algorithm is helpful to realize the fast calculation of far-field group matrix vector product, in order to further accelerate the filling of near-field group mutual impedance elements. The impedance matrix is further compressed by RACA algorithm, which is compared with the traditional FDM. The computation time and memory consumption of this method are reduced effectively. The second method is based on the feature basis function method to combine FDM with CBFM-II. The speed of matrix vector product between far-field groups in the construction of secondary eigenbase function and reduced matrix is accelerated, and the ACA algorithm is combined to further accelerate the filling of near-field group impedance matrix. The calculation efficiency is improved and the memory is saved.
【学位授予单位】：安徽大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN011

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