基于盲波束形成的波达方向--多普勒稳健估计

本文研究相控阵脉冲多普勒（ＰＤ）雷达多目标波达方向（ＤＯＡ）和多普勒频率同时估计问题,在未精确已知阵列流形条件下,提出了一种实现简单的盲波束形成方式的ＤＯＡ－Ｄｏｐｐｌｅｒ稳健估计新方法,理论分析和计算机仿真结果验证了其有效性。     

基于数据共轭重排的修正ESPRIT信号DOA估计算法

本文介绍了将接收数据共轭重排的再利用,构造相关矩阵的修正ESPRIT算法.理论分析和仿真实验表明,该算法同经典的ESPRIT算法相比,在快拍次数有限时,可明显改善信号DOA估计的性能,且其计算量二者基本相当.     

基于高阶统计的空间信号频率与二维到达方向估计

基于一个线性阵列、两个孤立阵元以及输出信号四阶累积量,本文提出了一种多个空间窄带信号频率、到达方位角和仰角估计方法.方法不仅使它们的估计无需谱峰搜索且自动配对,而且使阵列的阵元数大大减小,适合任意高斯噪声环境.计算机模拟结果证实了方法的可行性.     

一种基于双平行线阵相干源二维波达方向估计的新方法

提出一种基于双平行线阵有相干信号源存在情况下二雏波迭方向估计的新算法。该算法根据阵列结构的特点形成6个需要的相关矩阵，然后构造一个特殊大矩阵并经特征分解获得信号子空间的估计，最后利用2-D ESPRIT方法实现二维角度估计。无论是否存在相干源，新算法都能精确估计出二维到达角，而无需进行谱峰搜索，从而解决了信号参数配对问题。最后用计算机仿真验证了该算法的有效性。     

U-ESPRIT波达估计算法及其实现

介绍了"酉-利用旋转不变技术估计信号参数(U-ESPRIT)"算法的模型,U-ESPRIT算法是基于子空间分解的谱估计算法。在"利用旋转不变技术估计信号参数(ESPRIT)"算法的基础上,通过引入观测数据的复共轭信息,经过数学变换,使阵列输出信号的协方差矩阵由复矩阵变成实对称矩阵,减小了计算量。最后介绍了U-ESPRIT算法的工程实现,并进行了实际测试,实验结果表明该方法估计精度高,实现简单。     

基于改进的循环互相关MUSIC算法的波达方向估计

给出了基于循环互相关运算的信号模型，根据该信号模型，运用改进的循环互相MUSIC算法得到了对具有循环平稳特性的空间源信号波达方向进行估计的有效方法——ICCC-MUSIC算法；定性的分析和仿真实验均表明该方法具有较好的抑制噪声和选择信号的能力，为利用天线阵列提取具有循环平稳特性的源信号提供了较好的实现方法。     

基于子空间分解的阵列流形向量估计新方法

实际阵列装配完成后的阵列流形向量与理论值存在偏差,这种偏差会导致阵列预设波束图的旁瓣升高、阵列高分辨算法的性能下降,严重影响阵列的实际应用。实际中先依据估计的部分实际阵列流形向量选取合适的误差模型,再根据模型得到逼近实际的阵列流形向量。现有的实际阵列流形向量估计方法有直接定义法和最小二乘法,这两种方法计算复杂度很高且估计精度随快拍数波动。对此本文给出一种新的阵列实际流形向量估计方法,它利用阵列接收数据协方差矩阵的信号子空间与阵列流形向量张成空间相同的特性来估计阵列的实际幅度相位响应,结合估计的波达方向,最后得到实际的阵列流形向量。仿真结果表明,本文所提方法比现有的两种估计方法估计精度高一倍且计算复杂度降低了一个数量级。      

基于MIMO技术的二维波达方向估计

传统的L型阵相比面阵精简了阵列结构,以较少的阵元实现二维波达方向估计,但是波达方向估计性能受到物理孔径限制。本文将MIMO技术和L型阵结合,提出一种基于MIMO技术的L型阵二维波达方向估计方法。该方法通过MIMO等效虚拟阵列原理,将L型阵等效为一矩形平面阵列,然后在等效矩形阵列的基础上,采用MUSIC进行二维波达方向估计,以L型阵的物理孔径实现矩形平面阵列的估计性能。本文推导了二维波达方向估计的CRB,计算机仿真实验证实了所提方法的有效性。      

宽带阵列信号波达方向估计的新算法

随着信号处理技术的发展,阵列信号处理的许多领域如雷达、声纳等越来越需要更精确的估计空间宽带信号源的波达方向（direction of arrival,DOA）。CSM（coherent signal subspace method）聚焦类DOA估计算法需要预估计波达方向,预估角偏差会影响聚焦效果,严重时会导致算法失效,本文提出一种基于一致聚焦的DOA估计方法,该方法不需要预先估计到达角,通过聚焦矩阵将宽带信号聚焦到一个中心频率,然后可利用窄带DOA算法进行估计。该算法提高DOA估计的分辨率、估计精度,也增强了算法的实时性,仿真结果验证了此算法的有效性。     

一种快速的二维高分辨波达方向估计方法——混合波达方向矩阵法

本文利用两个具有位移特性的二维传感器子阵列信息构成一种新的矩阵－混合波达方向矩阵。混合波达方向矩阵的特征值和特征矢量分别为信号源方位角与高低角的混合方向元素和方位角与高低角的混合方向矢量。合理选择子阵列构造可以解决兼并信号源分辨问题和降低阵列孔径损失。     

A modified high-order cumulant music algorithm for signal DOA estimation

This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limited, this algorithm can improve the signal DOA estimation performances obviously, and its computational complexity scarcely increases. Finally, some simulation results to verify the theoretical analyses are presented.     

A method for solving DOA estimation ambiguity in esprit algorithm

This paper introduces a method for solving DOA estimation ambiguity in ESPRIT algorithm with the conventional beamformer. With the help of it, for any space of two subarrays, the signal DOA in [-π/2 ,π/2] can be estimated effectively by using ESPRIT algorithm. Finally, some simulation results to verify the theoretical analyses are presented.     

秩-1子空间跟踪算法

子空间估计和跟踪问题是在现代阵列信号处理应用中的一个重要课题。该文深入研究幂迭代算法并成功地将幂迭代用于子空间跟踪,分析和讨论了针对运动目标秩-1子空间的估计和跟踪及 DOA估计问题。通过计算机仿真实验证明了这种方法的有效性。     

锥面共形阵列信源方位和极化参数的联合估计算法

该文用交叉电偶极子对在锥面共形载体表面构造极化敏感阵列,在建立其快拍数据模型的基础上实现了信源方位和极化参数的联合估计。算法首先通过合理的矩阵变换将阵列流形中的信源方位和极化信息去耦合,然后分别根据秩损理论和旋转不变子空间思想对其进行估计,最后通过一种轮换比对配对方法实现信源方位和极化参数的联合估计。Monte-Carlo仿真实验表明,所提算法可以很好地解决锥面共形阵列的多参数联合估计问题。     

柱面共形阵列信源方位与极化状态的联合估计算法

针对柱面共形极化敏感阵列的结构特点建立了其导向矢量模型,以此为基础实现了信源方位与极化状态的联合估计。根据旋转不变子空间思想估计俯仰角和子空间原理、秩损理论,通过对阵列流形矩阵进行特定的形式变换,将其中隐含的信源方位信息与极化信息\     

BLIND CHANNEL ESTIMATION IN DELAY DIVERSITY FOR FREQUENCY SELECTIVE CHANNELS1

Delay diversity is an effective transmit diversity technique to combat adverse effects of fading. Thus far, previous work in delay diversity assumed that perfect estimates of current channel fading conditions are available at the receiver and training symbols are required to estimate the channel from the transmitter to the receiver. However, increasing the number of the antennas increases the required training interval and reduces the available time with in whichdata may be transmitted. Learning the channel coefficients becomes increasingly difficult for the frequency selective channels. In this paper, with the subspace method and the delay character of delay diversity, a channel estimation method is proposed, which does not use training symbols. It addresses the transmit diversity for a frequency selective channel from a single carrier perspective in the form of a simple equivalent flat fading model. Monte Carlo simulations give the performance of channel estimation and the performance comparison of our channel-estimation-based detector with decision feedback equalization, which uses the perfect channel information.     

信号DOA和极化参数的自适应估计

运用ESPRIT算法进行信号参数的估计,关键是对信号子空间的估计。在实际运用中,接收到的信号的参数往往是随时间变化的,因此要得到信号参数的实时估计值就需要根据阵列的接收数据对信号子空间进行更新。分析了如何运用ESPRIT算法得到信号的DOA和极化参数,并在矩阵扰动理论的基础上,利用矩阵特征分解一阶修正方法更新特征值和特征向量,从而使得ESPRIT算法能够自适应地估计信号DOA和极化参数。最后通过Matlab仿真验证了该方法的有效性。     

A new algorithm for 2-D DOA estimation

In this paper we present a new algorithm to estimate the 2-D direction of arrival (DOA) of narrowband sources lying in the far field of the array. The array consists of matched co-directional triplets, and can be considered as an extension of the 1-D ESPRIT scenario to 2-D. The proposed approach is simple and direct and does not require a search procedure or initialization. Existing algorithms require a search to match the correct elevation and azimuth angles and are computationally more expensive. This technique automatically pairs the azimuth and elevation angles by marking them. The computational complexity is twice that of 1-D ESPRIT. Simulation results and comparisons with other existing algorithms are presented to demonstrate the performance of the proposed technique.     

A novel approach for DOA estimation in unknown correlated noise fields

The key of the subspace-based Direction Of Arrival （DOA） estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.