【作者】 王丹；

【导师】 郭文彬；

【作者基本信息】 北京邮电大学 ， 信息与通信工程， 2020， 硕士

【摘要】 DOA(Direction-Of-Arrival,DOA)估计是阵列信号处理领域的基本问题,在无线通信、雷达、声纳、地震勘探等多个领域都受到了广泛的关注。在实际场景中,二维(Two-Dimensional,2D)DOA估计能更好地刻画三维空间中目标源信号的波达角方向。有很多二维阵列,但L型阵列因其结构简单、计算复杂度低和估计性能好得到了学者们的青睐。本文基于L型阵列,充分挖掘目标DOAs在二维角度域的稀疏特性,建立更加精确的L型阵列接收模型并设计相应的二维DOA估计方法,旨在获得更好的波达方向估计性能。论文针对离散角度域所带来的阵列接收模型拟合误差问题,分别研究了基于稀疏贝叶斯学习和无网格稀疏的L型阵列DOA估计方法。论文的主要研究成果如下:1、针对L型阵列下,针对基于稀疏表示的二维DOA估计阵列模型字典基不匹配的问题,本文充分考虑了均匀离散网格所带来的网格偏移量,提出了一种L型阵列的网格自适应模型,并提出了网格自适应的稀疏贝叶斯(Grid Adaptive Sparse Bayesian Learning,GASBL)二维DOA估计方法。该方法首先通过层级建模,进而在贝叶斯学习框架下实现二维DOA估计,并完成仰角和方位角的自动配对。仿真结果表明,本文所提的二维DOA估计方法GASBL能够实现波达角的更加精确的估计,且性能显著优于现有的DOA估计方法。2、针对角度域离散化所导致的接收模型误差问题,本文研究了能够直接在连续角度域进行DOA估计的无网格稀疏方法。为了克服网格划分所带来的模型拟合误差,我们将二维DOA视为二维角度域连续空间上的稀疏参数,建立了由稀疏参数表示的L型阵列接收模型,并提出了基于互相关矩阵和稀疏参数的无网格方法CCM-SPA。该方法先分别估计子阵列的DOA,然后根据互相关矩阵完成角度匹配。仿真结果表明,本文所提方法能够获得更好的DOA估计性能,不仅适用于均匀L型阵列(Uniform L-shape Array,ULA),还适用于稀疏 L 型阵列(Sparse L-shape Array,SLA)。更多还原

【Abstract】 Direction-of-arrival(DOA)estimation is a basic problem in the field of array signal processing.It has obtained increasing attention in a wide field of wireless communication,radar,sonar,seismic exploration and so on.In the actual scene,the two-dimensional(2D)DOA estimation can better characterize the DOA of the target signal in the three-dimensional space.Many antenna geometries for 2D-DOA estimation have been proposed,such as L-shape array,uniform circular array,parallel linear array,rectangular array,etc.Among these array geometries,2D-DOA estimation with L-shape array has gained its popularity for its high estimation accuracy,moderate complexity,and simple design.Based on the L-shape array,this paper fully exploits the sparse characteristics of the target DOA in the two-dimensional angle domain,then establishes a more accurate L-shape array model and studies the 2D-DOA estimation methods to obtain outstanding performance.In order to overcome the fitting error of the signal model caused by the discrete angle domain,this paper studies the L-shape array DOA estimation methods based on the sparse Bayesian learning and the sparse gridless.The main research results of the paper are as follows:1.Aiming at the problem of mismatching dictionary bases of 2D-DOA estimation array model based on sparse representation under L-shaped array,we propose a grid adaptive model to alleviate model error caused by uniform discrete grid.We also propose a grid adaptive sparse Bayesian learning(GASBL)method for 2D-DOA estimation method with L-shape array.This method realizes the estimation of elevation and azimuth by sparse Bayesian learning framework,and completes the angle pair automatically.Simulation results illustrate that our approach can achieve a better performance than the state-of-the-art methods.2.For the model error problem caused by the discretization of the angle domain this paper studies sparse gridless methods which can directly estimate DOAs in the continuous angle domain.In order to overcome the model fitting error,the 2D-DOA is regarded as a sparse parameter in the continuous space of the 2D angular domain.We establish an array model represented by sparse parameters for L-shape array.Then we propose a gridless sparse method named CCM-SPA which is based on cross-correlation matrix and Vandermonde decomposition.This method first estimates the DOA of the sub-array separately,and then match obtained elevation and azimuth according to the cross-correlation matrix.Simulation results show that the method proposed in this paper can achieve good performance,which is not only suitable for uniform L-shape array,but also for sparse L-shape array.更多还原