一种V型阵列的二维波达方向估计的新方法

波达方向估计是阵列信号处理的一个重要研究方向,在雷达、通信、声纳、地震勘测等领域都有着广泛的应用前景.它已成为阵列无源探测和智能天线中的关键技术.针对二维信号,本文研究一种基于V型阵二维波达方向估计的新算法.该算法根据阵列结构的特点形成多个需要的相关矩阵,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2D-ESPRIT方法实现二维角度估计,可以解决β角兼并信号的波达方向估计问题,无需谱峰搜索且信号参数自动配对.最后用计算机仿真验证了该算法的有效性.     

An improved SAR-GMTI method based on eigen-decomposition of the sample covariance matrix

An improved two-channel Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) method based on eigen-decomposition of the covariance matrix is investigated. Based on the joint Probability Density Function (PDF) of the Along-Track Interferometric (ATI) phase and the similarity between the two SAR complex images, a novel ellipse detector is presented and is applied to the indication of ground moving targets. We derive its statistics and analyze the performance of detection process in detail. Compared with the approach using the ATI phase, the ellipse detector has a better performance of detection in homogenous clutter. Numerical experiments on simulated data are presented to validate the improved performance of the ellipse detector with respect to the ATI phase approach. Finally, the detection capability of the proposed method is demonstrated by measured SAR data.     

基于邻域平均和正交分解的双通道SAR图像域慢动目标检测方法

慢动目标检测是利用合成孔径雷达(Synthetic Aperture Radar, SAR)实现空间对地观测应用的一个主要方面,具有广泛的应用背景和重要的学术价值。为了构建检测率高、实用性强的双通道SAR地面慢动目标检测过程,本文提出了一种基于邻域平均和协方差矩阵正交分解的检测算法。该算法在对特征值分解量进行修正的基础上,通过获取采样协方差矩阵与杂波协方差矩阵正交的分量,以此构造出有效的动目标检测量,结合采样协方差矩阵的邻域平均处理,实现慢动目标的精确检测。相比常规的DPCA(Displaced Phase Center Antenna)技术,该算法具有杂波抑制能力强、旁瓣抑制能力好、检测门限设定简单、检测率高、虚警率低等特点,仿真结果证明了该算法的有效性。     

一种基于修正数据矩阵的广义波达方向矩阵方法

该文提出了一种基于平面阵的相干信号二维DOA估计算法,文中先将平面阵分为3个具有旋转不变性的子阵列,并分别构造了3个子阵列的数据矩阵,结合这3个数据矩阵,构造了两种修正数据矩阵,提高了阵元利用率。然后仿照波达方向矩阵的构造方法,得到了一种广义波达方向矩阵。通过理论分析证明了对该矩阵进行特征分解,就可以获得信号的方向矢量和信号方向元素,从而能够进行相干信号的二维DOA估计,并且避免了谱峰搜索,减少了运算量,仿真结果验证了该算法的有效性和精确性。     

Maximum Margin Multisurface Support Tensor Machines with application to image classification and segmentation

Virtually all previous classifier models take vectors as inputs, performing directly based on the vector patterns. But it is highly necessary to consider images as matrices in real applications. In this paper, we represent images as second order tensors or matrices. We then propose two novel tensor algorithms, which are referred to as Maximum Margin Multisurface Proximal Support Tensor Machine (M³PSTM) and Maximum Margin Multi-weight Vector Projection Support Tensor Machine (M³VSTM), for classifying and segmenting the images. M³PSTM and M³VSTM operate in tensor space and aim at computing two proximal tensor planes for multisurface learning. To avoid the singularity problem, maximum margin criterion is used for formulating the optimization problems. Thus the proposed tensor classifiers have an analytic form of projection axes and can achieve the maximum margin representations for classification. With tensor representation, the number of estimated parameters is significantly reduced, which makes M³PSTM and M³VSTM more computationally efficient when handing the high-dimensional datasets than applying the vector representations based methods. Thorough image classification and segmentation simulations on the benchmark UCI and real datasets verify the efficiency and validity of our approaches. The visual and numerical results show M³PSTM and M³VSTM deliver comparable or even better performance than some state-of-the-art classification algorithms.     

On-line adaptive principal component extraction algorithms using iteration approach

Two new on-line algorithms for adaptive principal component analysis (APCA) are proposed and discussed in order to solve the problem of on-line industrial process monitoring in this paper. Both the algorithms have the capability of extracting principal component eigenvectors on-line in a fixed size sliding data window with high dimensional input data. The first algorithm is based on the steepest gradient descent approach, which updates the covariance matrix with deflation transformation and on-line iteration. Based on neural networks, the second algorithm constructs the input data sequence with an on-line iteration method and trains the neural network in every data frame. The convergence of the two algorithms is then analyzed and the simulations are given to illustrate the effectiveness of the two algorithms. At last, the applications of the two algorithms are discussed.     

大规模数据集下谱聚类算法的求解

谱聚类算法是一种流行的数据聚类方法,该算法使用特征分解技术计算邻接矩阵的特征解,但是在大规模数据集的情况下,因储存和计算的问题而无法进行求解。基于线性代数中对称矩阵的性质,提出使用部接矩阵的每一列作为迭代算法的输入样本,通过迭代计算出部接矩阵的特征解。所提算法的空间复杂度只有O(m),时间复杂度也降低为O(pkm)。实验结果验证了算法的有效性。     

利用样本协方差矩阵特征值分解实现双通道SAR动目标检测

该文针对机载双通道SAR-GMTI系统及实测数据,提出一种新的地面慢动目标检测方法。该方法基于双通道样本协方差矩阵特征值分解,将杂波第2特征值和干涉相位联合统计特性的研究结果用于慢动目标检测,即根据给定的恒虚警概率确定一条联合分布的第2特征值干涉相位等高线作为门限检测曲线,同时结合第2特征值、干涉相位门限预处理,实现地面慢动目标的精确检测。实测数据实验结果表明:该方法不但扩大了慢动目标的可检测速度范围,同时还降低了系统的虚警概率。     

罗兰C接收机天波识别技术研究

使用现代谱估计技术进行罗兰C接收机的天波延迟估计,有效地解决了常规接收机基准点固定的问题。文章提出的方法,在不断变化的天波干扰环境下根据数据调节相应的采样点,在低信噪比条件下分离出了地波和天波的到达时刻,且具有较高的分辨率。本文对基于参数建模和特征值分解的算法进行了讨论,充分说明了现代谱估计技术能减少对准基准点的时间,可提高现有罗兰C接收机的性能。     

基于单通道SAR图像序列特征值分解的动目标检测方法

该文提出了一种基于单通道图像序列间协方差矩阵分解的动目标检测方法。首先给出基于方位频谱划分获取子图像的处理过程,分析了子孔径划分在图像序列间所产生的误差来源,结合二维自适应方法对幅度和相位上存在的误差同时校正,实现了子图像间的配准,构造出类似于多通道的子图像。在此基础上,结合多通道杂波抑制的思想,详细分析了两子孔径间协方差矩阵特征值分解实现目标与杂波分离的原理,并针对在图像域估计采样协方差引起的精度与目标能量损失之间的矛盾,提出了在距离多普勒域的改进处理。最后,经过仿真实验验证了该方法的有效性。