宽带阵列信号DOA估计算法研究

发布时间：2018-03-25 18:32

本文选题：宽带信号　切入点：DOA估计　出处：《南京大学》2017年硕士论文

【摘要】：阵列信号处理技术的应用涉及广泛,在军事及国民经济领域均有重大发展。作为阵列信号处理的重要分支,波达方向(Direction of Arriva1,DOA)估计已成为热门研究课题。目前关于窄带信号空间测向的算法已趋于成熟,而对于携带更多有效信息、具有更高应用价值的宽带信号,传统的窄带信号算法对其并不适用,需要研究新的适用于宽带信号的高分辨DOA估计算法。本文重点研究宽带信号的高分辨DOA估计算法,研究内容包括:1.对传感器构成的阵列进行理想化假设,分别对宽带及窄带信号构建信号模型。研究经典窄带高分辨测向算法,通过实验仿真分析阵列信号模型各类参数对MUSIC、ESPRIT算法估计精度的影响,该方法可用于基于子带的宽带DOA估计。2.分析空间平滑技术实现信号解相干时所能达到的信源数上限,研究基于矩阵共轭重构实现信号解相干的修正MUSIC算法。进行了实验仿真,分析前向平滑、前后向平滑以及修正MUSIC三种算法的性能。3.分析了处理宽带信号的非相干子空间算法(ISM),并基于修正MUSIC改进ISM算法,弥补其无法解相干的缺陷。鉴于相关子空间算法(CSM)构造的聚焦矩阵并不是唯一的,分别研究以阵列流型及去噪后的接收数据为基准的RSS、TCT算法,通过仿真对比两类算法在不同信噪比下所表现的性能差异。4.传统MUSIC算法的统计特性是建立在阵元数固定、采集的样本数趋于正无穷的基础上的。在实际应用中,阵列的阵元数较大,受硬件限制,阵列采集的样本数有限,无法满足快拍数远大于阵元数的条件。当样本数较小时,MUSIC算法的估计精度会出现较大偏差。本文提出一种改进MUSIC算法,在采集的样本数有限的情况下,通过改进接收的离散数据所分解的信号子空间,提高DOA的估计精度。对CSM聚焦后的频域窄带模型数据采用本文改进算法进行了实验仿真,结果表明改进的CSM具有更高的估计精度。
[Abstract]:Array signal processing technology has been widely used in military and national economy. As an important branch of array signal processing, Direction of arrival (DOA) estimation has become a hot research topic. At present, the algorithm of direction finding for narrowband signals has become mature, while for wideband signals with more effective information, it has higher application value. The traditional narrowband signal algorithm is not suitable for it, so it is necessary to study a new high-resolution DOA estimation algorithm for wideband signals. This paper focuses on the high-resolution DOA estimation algorithm for wideband signals. The research contents include: 1.The ideal assumption of the sensor array is given, and the signal models of wideband and narrowband signals are constructed, respectively, and the classical narrowband high-resolution direction-finding algorithm is studied. The influence of various parameters of array signal model on the estimation accuracy of MUSICI Esprit algorithm is analyzed by experimental simulation. The method can be used to estimate wideband DOA based on subband. 2. The upper limit of source number can be obtained when spatial smoothing technology is used to realize signal decoherence. The modified MUSIC algorithm based on matrix conjugate reconstruction to realize signal decoherence is studied. The experimental simulation is carried out and the forward smoothing is analyzed. The performance of three algorithms, forward smoothing and modified MUSIC. 3. The incoherent subspace algorithm for wideband signal processing is analyzed, and the modified ISM algorithm is improved based on modified MUSIC. In view of the fact that the focusing matrix constructed by the correlation subspace algorithm (CSM) is not unique, the RSS-TCT algorithm based on the array flow pattern and the de-noised received data is studied respectively. The statistical characteristics of the traditional MUSIC algorithm are based on the fixed number of array elements, and the number of samples collected tends to be infinitely positive. The array has a large number of elements and is limited by hardware, so the number of samples collected by the array is limited. It is impossible to satisfy the condition that the number of beats is far larger than the number of elements of the matrix. When the number of samples is small, there will be a large deviation in the estimation accuracy of music algorithm. In this paper, an improved MUSIC algorithm is proposed, in which the number of samples collected is limited. The estimation accuracy of DOA is improved by improving the signal subspace decomposed by the received discrete data. The experimental simulation of the CSM focused narrow-band model data in frequency domain is carried out by using the improved algorithm in this paper. The results show that the improved CSM has higher estimation accuracy.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.7

【参考文献】