OFDM信号空间测向技术研究

发布时间：2018-04-25 05:28

本文选题：非均匀噪声 + Khatri-Rao子空间　；参考：《电子科技大学》2017年硕士论文

【摘要】：正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)是一种效率很高的多载波调制技术。因其具有抗频率选择性衰落、频谱利用率高等特点,所以备受阵列信号处理方向的研究人员关注。如何有效地估计OFDM信号的来波方向,已成为阵列信号处理中一个新的研究方向。本文考虑实际工程应用的需求,主要围绕非均匀噪声情况下和欠定情况下的OFDM信号波达方向估计问题展开研究,具体研究内容如下:1、研究了现有的一些宽带信号空间测向算法。如ISM算法、修正的ISM算法以及CSM算法。通过对这些算法进行推导和仿真分析,可知这些算法只适用于信源数小于阵元数情况下的空间测向。且当天线阵元噪声为非均匀噪声时,这些算法都会产生性能恶化。这为后续研究OFDM信号波达方向估计算法提供了理论基础和参考依据。2、针对非均匀噪声背景下,一些经典的测向算法可能会产生估计偏差增大、角度分辨率下降以及出现伪峰等问题,提出了基于MUSIC-Like算法的OFDM信号空间测向算法。该算法先利用虚拟阵列平移方法进行解相关处理,再通过波束形成技术以及拉格朗日乘子法技术,求出每个子窄带的最优空域滤波器的权向量,最后利用加权求平均运算得到测向的空间谱。仿真结果表明,该算法不仅在非均匀噪声背景有较好的估计精度,而且能估计出相干信号。此外,该算法实现了信源未知条件下的空间测向,且不依赖于子空间分解。这就避免了因错误估计信源数而对OFDM信号波达方向估计性能产生的影响,提高了信号波达方向估计的分辨率。3、针对欠定情况下,即信源数大于阵元数,大多数现有的算法(如ISM算法等)不能估计出目标的波达方向的问题,提出了基于稀疏重构的Khatri-Rao子空间OFDM信号空间测向算法。该算法首先针对二阶嵌套阵列提出了一种改进的嵌套阵列用于提高阵列的自由度;然后结合Khatri-Rao积构造一个新的虚拟阵列流形矩阵;最后利用稀疏重构算法进行空间测向。仿真结果表明,基于稀疏重构的KR子空间OFDM信号空间测向算法可以在欠定和非均匀噪声背景下较好地估计出信号的波达方向,并且其估计精度较现有算法有较大提高。
[Abstract]:Orthogonal Frequency Division Multiplexing (OFDM) is an efficient multicarrier modulation technique. Because of its characteristics of anti-frequency selective fading and high spectral efficiency, it has attracted the attention of researchers in the field of array signal processing. How to estimate the direction of OFDM signal effectively has become a new research direction in array signal processing. In this paper, the direction of arrival (DOA) estimation of OFDM signals in the case of non-uniform noise and underdetermined signal is studied in consideration of the requirements of practical engineering applications. The specific research contents are as follows: 1, and some existing spatial direction-finding algorithms for wideband signals are studied. Such as ISM algorithm, modified ISM algorithm and CSM algorithm. Through the derivation and simulation analysis of these algorithms, it can be concluded that these algorithms are only suitable for spatial direction finding when the number of sources is less than the number of array elements. When the noise of antenna array is nonuniform, the performance of these algorithms will deteriorate. This provides a theoretical basis and reference basis for further research on DOA estimation of OFDM signals. For non-uniform noise background, some classical direction-finding algorithms may cause an increase in estimation deviation. The spatial direction finding algorithm of OFDM signal based on MUSIC-Like algorithm is proposed. The algorithm first uses the virtual array translation method to decorrelate, and then through the beamforming technology and Lagrange multiplier method, the weight vector of each sub-narrow band optimal spatial filter is obtained. Finally, the spatial spectrum of direction finding is obtained by weighted averaging operation. The simulation results show that the proposed algorithm not only has good estimation accuracy in non-uniform noise background, but also can estimate coherent signals. In addition, the algorithm realizes spatial direction finding under unknown source conditions, and does not depend on subspace decomposition. This avoids the influence on the DOA estimation performance of OFDM signal due to the wrong estimation of the number of sources, and improves the resolution of DOA estimation of the OFDM signal. In the case of uncertainty, the number of sources is larger than the number of array elements. Most of the existing algorithms (such as ISM algorithm) can not estimate the DOA of the target. A spatial direction finding algorithm of Khatri-Rao subspace OFDM signal based on sparse reconstruction is proposed. Firstly, an improved nested array is proposed to improve the degree of freedom of the second order nested array, then a new virtual array manifold matrix is constructed based on Khatri-Rao product. Finally, the sparse reconstruction algorithm is used for spatial direction finding. The simulation results show that the KR subspace OFDM signal spatial direction finding algorithm based on sparse reconstruction can estimate the direction of arrival (DOA) of the signal under the background of unsteady and non-uniform noise, and its estimation accuracy is much higher than that of the existing algorithms.
【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】：TN929.53

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