宽带雷达回波散射中心特征提取方法研究

发布时间：2018-06-12 15:08

本文选题：高分辨率 + 散射中心　；参考：《西安电子科技大学》2014年硕士论文

【摘要】：随着理论研究的不断深入和雷达信号分辨率的不断提升,基于宽带高分辨率雷达信号的目标识别成为了雷达信号处理领域的一个重要发展方向。宽带高分辨率雷达获取的目标回波不再是一个“点”,而是由一系列分布在不同距离单元上的目标散射点构成的高维信号。通过提取并分析目标回波中的散射点可以获取反映目标本质属性的特征,如目标体的物理尺寸以及典型部件的尺寸和结构等信息。本文正是基于宽带高分辨率雷达回波数据,研究了目标散射中心特征的提取方法。论文主要内容概括如下:1.分析比较了四种稀疏求解算法,并引入交替投影法改进算法。首先介绍了四种稀疏求解算法的基本原理和操作流程,结合实验比较了各个算法的应用效果。然后提出了一种结合交替投影法的稀疏求解方法,通过不同的实验设置,验证了所提方法能够提高稀疏求解的精度,并在一定程度上改善了求解算法对噪声的稳健性。2.研究了一维简单散射中心特征的提取方法。介绍了一维散射中心的几何绕射模型,以及极化散射矩阵的基本概念;提出了一种联合多极化通道高分辨率距离像的散射中心提取方法。通过仿真实验证明,与传统方法相比所提方法在目标散射中心位置和极化散射矩阵的估计方面有一定优势。3.研究了属性散射中心提取方法。(1)提出了一种结合交替投影与正交匹配追踪的属性散射中心提取方法。首先提取目标的合成孔径雷达(Synthetic Aperture Radar,SAR)图像支撑区实现对目标参数的自动初始化,减少了字典维度,降低了计算复杂度;然后利用正交匹配追踪法估计目标属性散射中心参数,最后通过交替投影法对所估参数进行修正。该方法能够降低目标属性散射中心间的高度耦合对散射中心参数估计的影响,提高了估计精度。(2)提出了一种结合图像信息的频域属性散射中心特征提取方法。该方法在频域方法的估计过程中,引入目标的图像信息实现对散射中心间的去耦合,既降低了计算复杂度,也改善了参数估计精度。
[Abstract]:With the deepening of theoretical research and the improvement of radar signal resolution, target recognition based on wideband high resolution radar signal has become an important development direction in the field of radar signal processing. The target echo obtained by wideband high-resolution radar is no longer a "point", but a high-dimensional signal composed of a series of scattering points distributed on different distance units. By extracting and analyzing the scattering points in the echo of the target, the characteristics reflecting the essential properties of the target can be obtained, such as the physical size of the target body, the size and structure of the typical components, and so on. Based on the wideband high resolution radar echo data, this paper studies the extraction method of target scattering center feature. The main contents of this paper are summarized as follows: 1: 1. Four sparse solving algorithms are analyzed and compared, and the alternating projection method is introduced to improve the algorithm. Firstly, the basic principle and operation flow of four sparse solving algorithms are introduced, and the application results of each algorithm are compared with experiments. Then a sparse solution method combined with alternating projection method is proposed. Through different experimental settings, it is verified that the proposed method can improve the accuracy of sparse solution, and improve the robustness of the algorithm to noise to a certain extent. A method for extracting the feature of one-dimensional simple scattering center is studied. The geometric diffraction model of one-dimensional scattering center and the basic concept of polarimetric scattering matrix are introduced, and a method of extracting scattering center with high resolution range profile of multi-polarization channel is proposed. The simulation results show that the proposed method has some advantages over the traditional method in estimating the location of the target scattering center and the polarization scattering matrix. In this paper, the method of extracting attribute scattering center is studied. (1) an attribute scattering center extraction method combining alternating projection and orthogonal matching tracking is proposed. Firstly, the SAR synthetic Aperture radar (SAR) image support region of the target is extracted to realize the automatic initialization of the target parameters, which reduces the dictionary dimension and computational complexity, and then estimates the scattering center parameters of the target attributes by orthogonal matching tracking method. Finally, the estimated parameters are modified by alternating projection method. This method can reduce the influence of high coupling between scattering centers of target attributes on the estimation of scattering center parameters and improve the estimation accuracy. In the estimation process of frequency-domain method, the image information of the target is introduced to realize the decoupling between scattering centers, which not only reduces the computational complexity but also improves the precision of parameter estimation.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957.51

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