基于协方差矩阵稀疏重构的DOA估计算法

发布时间：2019-06-02 11:07

【摘要】：近年来,随着信息技术的飞速发展,传统的DOA估计算法由于需要大量的采样数据、处理相干信号效果差、需要较高的信噪比等原因,在一些新兴领域的应用受到了很大的限制。因此,如何从有限的观测数据中高分辨地进行信号参数估计,就成为一个重要的发展方向。随着压缩感知理论的兴起与发展,为DOA估计技术的发展打开了一个广阔的空间。理论指出,在条件允许的情况下,稀疏或可压缩信号可以从有限的采样数据中高精确地恢复出来。为克服传统算法中存在的不足,有效利用信号的空域稀疏特性,本文采用压缩感知理论进行信号的参数估计。现将本文研究的主要内容和所完成工作列举如下:1.从理论发展历史出发,对DOA估计算法、压缩感知理论的相关背景知识做了简单概述。2.简要分析了阵列信号处理的基本理论,给出基本的均匀线阵的数学模型,并对传统的窄带信号的DOA估计算法进行介绍,其中包括几种经典的处理相干与非相干窄带信号的算法。3.着重分析了压缩感知理论中的三大核心要素,对稀疏重构的条件RIP特性和MIP特性做了简单分析。同时,针对信号的空域稀疏特性,建立重构模型,并对经典的基于压缩感知理论的L1_SVD算法做了简单的介绍。4.由经典的基于协方差矩阵稀疏重构的L1_SRACV算法出发,引出对这类算法的改进措施:如利用KR积变换、协方差矩阵形式变换等方法,得到新的观测向量和冗余字典,并利用大量的仿真实验说明改进后算法的优缺点。
[Abstract]:In recent years, with the rapid development of information technology, the application of traditional DOA estimation algorithm in some new fields is limited because of the need for a large number of sampling data, the poor effect of processing coherent signals, and the need for high signal-to-noise ratio (SNR). Therefore, how to estimate the signal parameters from the limited observation data with high resolution has become an important development direction. With the rise and development of compressed perception theory, it opens up a broad space for the development of DOA estimation technology. It is pointed out that sparse or compressed signals can be recovered from limited sampled data with high accuracy when conditions permit. In order to overcome the shortcomings of the traditional algorithm and make effective use of the spatial sparse characteristics of the signal, the compressed perception theory is used to estimate the parameters of the signal. The main contents and work completed in this paper are listed as follows: 1. Based on the history of theoretical development, the DOA estimation algorithm and the related background knowledge of compressed perception theory are briefly summarized. 2. The basic theory of array signal processing is briefly analyzed, the basic mathematical model of uniform linear array is given, and the traditional DOA estimation algorithm of narrowband signal is introduced. It includes several classical algorithms for dealing with coherent and incoherent narrowband signals. The three core elements of compression perception theory are analyzed, and the conditional RIP characteristics and MIP characteristics of sparse reconstruction are briefly analyzed. At the same time, according to the spatial sparse characteristics of the signal, the reconstruction model is established, and the classical L1_SVD algorithm based on compressed perception theory is briefly introduced. 4. Based on the classical L1_SRACV algorithm based on sparse reconstruction of covariance matrix, the improvement measures of this kind of algorithm are introduced, such as using KR product transformation, covariance matrix formal transformation and other methods to obtain new observation vectors and redundant dictionaries. A large number of simulation experiments are used to illustrate the advantages and disadvantages of the improved algorithm.
【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】：TN911.23

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