基于压缩感知的阵列天线信号参数估计算法研究
发布时间：2018-07-15 19:45
【摘要】：信号参数估计是阵列信号研究中一个重要的组成部分,随着实际应用需求的增加,各领域对于参数估计系统的要求也越来越高,因而为得到更精确的信源定位,参数估计算法的研究备受重视。而传统的以MUSIC算法及ESPRIT算法为代表的算法,经过改进后已具备比较高的估计分辨率以及精度。但是这些算法对于信噪比以及快拍等条件要求比较苛刻,并且相干信源情况下得不到准确的估计结果。而近年来兴起的压缩感知理论,通过稀疏重构的方式实现信号参数的估计,算法的优点在于,仅仅需要阵列的单次或少次快拍的数据,并且具有天然的解相干能力。本文对这类算法的个别关键技术进行了讨论以及分析,主要的工作如下:1.给出传统信号参数估计的模型,介绍了压缩感知理论的相关理论基础,在此基础上介绍了基于压缩感知理论的信号参数估计模型,并且分析了与传统模型的区别与联系,为后续的研究奠定了理论的基础。2.针对标量阵列的DOA估计,介绍了几种基于压缩感知理论的常见的DOA估计算法,并通过仿真验证压缩感知算法相对于传统估计算法在例如分辨率和相干信源估计上的优势,对比分析了几种算法的估计性能,并简要分析了算法的优劣势,为实际工程应用中合理的选择适当的算法提供了理论依据。3.针对极化敏感阵列的多参数估计,本文介绍了极化敏感阵元的结构以及阵列信号的接收数据模型,并在此基础上将压缩感知理论推广到极化敏感阵列信号多参数估计中。对信号的接收数据模型进行重新建模,分别根据不同的极化阵元研究两种信号的稀疏表示形式及多参数估计算法,实现了电磁极化信号的空域到达角,以及极化信息的估计,并通过仿真实验证明相对于传统算法本文算法性能有所提高,简要分析两种算法的应用范围及算法估计性能等。4.针对存在互耦效应下的阵列信号参数估计,介绍互耦误差阵列的信号接收数据模型,并且针对未知互耦信息的情况,分别利用激励矩阵和阵列导向矢量变换两个角度研究互耦误差存在条件下的信号的稀疏表示形式以及信源定位算法,提高了未知互耦信息条件下信源方位估计的准确性,最后通过仿真对比两种处理方式下算法的估计性能。
[Abstract]:Signal parameter estimation is an important part of array signal research. With the increasing demand of practical application, the requirement of parameter estimation system in various fields is higher and higher, so more accurate source location can be obtained. The research of parameter estimation algorithm has attracted much attention. The traditional algorithms represented by music algorithm and Esprit algorithm have higher resolution and accuracy after improvement. However, these algorithms are demanding for SNR and rapid-shoot conditions, and can not get accurate estimation results in the case of coherent sources. In recent years, the compression sensing theory, which realizes the estimation of signal parameters by sparse reconstruction, has the advantage that it only needs the data of single or few shot of the array, and it has the natural ability of decoherence. In this paper, some key techniques of this algorithm are discussed and analyzed. The main work is as follows: 1. This paper gives the model of traditional signal parameter estimation, introduces the related theoretical basis of compression perception theory, and then introduces the signal parameter estimation model based on compression perception theory, and analyzes the difference and relation between the model and the traditional model. For the subsequent research laid the theoretical foundation. 2. For the DOA estimation of scalar array, several common DOA estimation algorithms based on compressed sensing theory are introduced, and the advantages of compressed sensing algorithms compared with traditional estimation algorithms such as resolution and coherent source estimation are verified by simulation. The estimation performance of several algorithms is compared and analyzed, and the advantages and disadvantages of the algorithms are briefly analyzed, which provides a theoretical basis for the reasonable selection of appropriate algorithms in practical engineering applications. In this paper, the structure of polarization-sensitive array elements and the receiving data model of array signals are introduced, and the theory of compression sensing is extended to the multi-parameter estimation of polarization-sensitive array signals. The received data model is remodeled and the sparse representation of two signals and multi-parameter estimation algorithms are studied according to different polarization array elements respectively. The spatial arrival angle and polarization information estimation of electromagnetic polarization signal are realized. The simulation results show that the performance of this algorithm is better than that of the traditional algorithm. The application scope of the two algorithms and the estimation performance of the two algorithms are analyzed briefly. 4. For the parameter estimation of array signal with mutual coupling effect, the signal receiving data model of mutual coupling error array is introduced, and the case of unknown mutual coupling information is discussed. The sparse representation of signals with mutual coupling error and the source location algorithm are studied by using the excitation matrix and array steering vector transform respectively. The accuracy of the source azimuth estimation under the condition of unknown mutual coupling information is improved. Finally, the estimation performance of the two algorithms is compared by simulation.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN820.15
本文编号：2125184
[Abstract]:Signal parameter estimation is an important part of array signal research. With the increasing demand of practical application, the requirement of parameter estimation system in various fields is higher and higher, so more accurate source location can be obtained. The research of parameter estimation algorithm has attracted much attention. The traditional algorithms represented by music algorithm and Esprit algorithm have higher resolution and accuracy after improvement. However, these algorithms are demanding for SNR and rapid-shoot conditions, and can not get accurate estimation results in the case of coherent sources. In recent years, the compression sensing theory, which realizes the estimation of signal parameters by sparse reconstruction, has the advantage that it only needs the data of single or few shot of the array, and it has the natural ability of decoherence. In this paper, some key techniques of this algorithm are discussed and analyzed. The main work is as follows: 1. This paper gives the model of traditional signal parameter estimation, introduces the related theoretical basis of compression perception theory, and then introduces the signal parameter estimation model based on compression perception theory, and analyzes the difference and relation between the model and the traditional model. For the subsequent research laid the theoretical foundation. 2. For the DOA estimation of scalar array, several common DOA estimation algorithms based on compressed sensing theory are introduced, and the advantages of compressed sensing algorithms compared with traditional estimation algorithms such as resolution and coherent source estimation are verified by simulation. The estimation performance of several algorithms is compared and analyzed, and the advantages and disadvantages of the algorithms are briefly analyzed, which provides a theoretical basis for the reasonable selection of appropriate algorithms in practical engineering applications. In this paper, the structure of polarization-sensitive array elements and the receiving data model of array signals are introduced, and the theory of compression sensing is extended to the multi-parameter estimation of polarization-sensitive array signals. The received data model is remodeled and the sparse representation of two signals and multi-parameter estimation algorithms are studied according to different polarization array elements respectively. The spatial arrival angle and polarization information estimation of electromagnetic polarization signal are realized. The simulation results show that the performance of this algorithm is better than that of the traditional algorithm. The application scope of the two algorithms and the estimation performance of the two algorithms are analyzed briefly. 4. For the parameter estimation of array signal with mutual coupling effect, the signal receiving data model of mutual coupling error array is introduced, and the case of unknown mutual coupling information is discussed. The sparse representation of signals with mutual coupling error and the source location algorithm are studied by using the excitation matrix and array steering vector transform respectively. The accuracy of the source azimuth estimation under the condition of unknown mutual coupling information is improved. Finally, the estimation performance of the two algorithms is compared by simulation.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN820.15
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