【作者】 李浩；

【导师】 叶中付；

【作者基本信息】 中国科学技术大学 ， 信息与通信工程， 2016， 硕士

【摘要】 波达方向估计是阵列信号处理中的一个重要研究方向,但现有的算法大多基于多通道系统,每个阵元后都需要相应的采样电路和模数转换电路(即“通道”),这在一些小型化或低成本的系统中会增加硬件规模,也为射频电路的设计带来困难。使用传感器阵列单通道接收系统是解决该问题的一种方案,它以牺牲一部分性能为代价,降低了系统硬件复杂度。在单通道系统中,由幅相因子组成的权矢量在前端对信号进行加权求和,将求和后的数据传入后端电路,这样射频系统只需要一路采样和数模转换电路即可,而且减小了由于通道的不一致性导致的幅相误差问题。近年来,稀疏恢复和压缩感知理论不断完善,为解决波达方向估计问题开辟了新的路径,弥补了传统方法的许多缺点。但目前基于稀疏恢复理论的单通道系统测向算法尚不完善,本文在现有成果的基础上,对该问题进行了进一步研究,主要工作如下：1.对于窄带信号,我们从相关域出发,构建了适用于单通道系统的阵列协方差矩阵列向量的稀疏模型,并推导了模型误差的概率分布,从而将波达方向估计转化为误差约束下矩阵l1.2混合范数最小化的问题,并使用现有的凸优化工具包进行求解。随后,我们结合波束形成原理和压缩感知理论,研究了权矢量的选取,使权矢量形成主副瓣比适中的波束,并均匀覆盖整个方向空间,得到了稳定的估计结果。实验表明所提算法相比其他单通道算法,具有更低的信噪比门限,且同时适用于非相干信号和相干信号。2.对于信号频率未知的情况,我们针对慢衰落信道,提出一种单通道系统角度-频率联合估计算法。首先从数据域出发,对于波束主瓣范围内的信号,构建了单个权矢量下的角度-频率二维稀疏模型,得到一个便于求解的LASSO问题。随后对于任意方向的信号,通过多个权矢量形成多个覆盖全空间的波束,分别处理对应主瓣范围内的信号,最终通过重构误差判断信号的真实性,进行结果的融合。3.研究了宽带信号的单通道波达方向估计问题。在所提的窄带信号波达方向估计算法的基础上,分别使用ISSM方法和CSSM方法进行分频处理。仿真实验表明,CSSM方法更好地利用了子频带之间的联合信息,尤其是在小快拍和低信噪比的情况下,具有更好的估计性能。更多还原

【Abstract】 Direction-of-arrival (DOA) estimation is an important research field in array signal processing. Most of the DOA estimation algorithms so far are based on multi-channel array systems, where every array element is followed by a circuit, the so-called "channel", in order to sample the received signal and convert it from analog to digital. However, in micro systems and low-cost devices, this may increase hardware scales and bring difficulty in the design of ratio frequency circuits. Single channel array receiver offers an alternate against these problems at the cost of some processing ability and accuracy, where multiple antennas share a single channel receiver by combining them with a weight vector in order to present the receiver with a single signal. Moreover, this will reduce the channel mismatch in multi-channel systems.In recent years, sparse recovery and compressed sensing theory provide a new perspective for DOA estimation and achieve better performance in many circumstances. But DOA estimation algorithms based on single channel array using sparse recovery are not sufficient yet. We made further research in this field and summarized the results as follows.1. For narrow-band signals, we constructed a sparse model for the column vectors of the array covariance matrix which is compatible for single systems. Then the model error is derived in order to convert the problem of DOA estimation into one of l1.2 mixed norm minimization under a given error constrain which can be solved by convex optimization. The selection of weight vectors is also considered based on the principles of beamforming and compressed sensing theory. We designed a series of weight vectors which formed appropriate beam patterns to cover the whole angle space and achieved stable estimates. Simulations showed that the proposed method had better performance than other single channel methods, especially when SNR is low, and is applicable for both uncorrelated and correlated signals.2. When the frequency of signals are unknown, we proposed a joint DOA-FOA estimation method for slow-fading signals. Firstly, based on single weight vector, for signals coming from the main lobe of the beam pattern, we constructed an angle-frequency 2D sparse model which is a typical LASSO problem and is easy to solve. Then, for signals of arbitrary directions, a series of weight vectors are used to handle the corresponding areas. Finally, reconstruction error is calculated to pick the real signal directions.3. For wideband signals, we divide them into a series of narrow-band signals and then utilized both ISSM and CSSM method based on the algorithm proposed before. Simulation shows that CSSM method makes better use of the joint information between different bands and achieves better performance, especially when signal-to-noise ratio is low and the number of snapshots is small.更多还原