带旁瓣抑制的稳健匹配场定位算法

在特征值空间中,利用接收数据协方差阵的主特征向量,与扰动环境参数生成的拷贝场向量协方差阵的主特征向量,构造了一种巴特利特型的匹配场定位算法,它对环境参数失配稳健,但是带有很高的旁瓣级。引入一种约束优化机制,以最大化目标区域的空间平均的输出功率与搜索区域空间平均的功率比为目标,得到一个经过优化的观测数据向量。使用数据向量对定位表面的旁瓣进行约束抑制,在保持目标区域输出功率的同时,有效降低搜索区域的背景功率。实测数据分析表明,该算法能有效抑制旁瓣,提高定位性能。     

DOA估计算法的一种修正MUSIC算法的研究

传统改进 MUSIC 算法通过对接收信号协方差矩阵作预处理,使信号协方差矩阵分解得到信号子空间与噪声子空间正交,从而降低噪声的影响。但当信号间隔很小时,随着信噪比的降低,传统改进MUSIC算法已无法分辨出信号。基于此问题提出的修正MUSIC算法在使信号子空间与噪声子空间正交的基础上,充分利用了噪声子空间及其特征值对噪声子空间的修正,进而构造谱峰搜索函数估计出信号。通过仿真实验,证实了在信噪比很低的情况下,信号间隔很小且存在相关信号时,修正MUSIC算法能准确地估计出传统改进MUSIC算法不能估计的信号。     

一种稳健的波束域自适应波束形成算法

在波束域算法中,针对波束域期望信号的指向误差落在波束主瓣边缘时波束性能严重恶化,采用波束域旋转矢量法的线性约束来改善波束域自适应算法的性能,同时为提高工程的实施性,减少算法的计算量,利用信号的特征值大于噪声的特征值这一理论,采用空间协方差矩阵逆的高阶次幂来逼近信号子空间,将求得的权矢量投影于改进的波束域的特征信号子空间,该算法在波束域中不但减少了计算量,而且使波束具有更好的信号比和稳健性。实验仿真验证了提出的改进波束域特征空间的稳健自适应算法的正确性和有效性,具有一定的工程实用价值。     

一种简化特征空间稳健自适应波束形成算法*

在基于特征空间(ESB)的自适应波束形成算法中,针对当指向误差落在波束主瓣的边缘特定角度时,输出信干噪比下降,且信号子空间需要进行费时的特征值分解的问题,提出了改进线性约束最小方差(LCMV)算法。在假定的期望信号方向附近减少一个方向性约束条件,并基于信号特征值大于噪声特征值的这一特性, 利用空间协方差矩阵逆的高阶次幂来逼近信号子空间,无须特征分解,将求得的权矢量向改进的信号子空间投影。该方法能够大大减少计算量,同时还显著提高了自适应波束形成稳健性。通过仿真分析及结果比较验证了算法的正确性和有效性,因此从     

基于非负特征值分解的极化SAR子空间分解滤波

极化SAR子空间分解滤波的优势在于能很好地保持极化信息,然而斑点噪声抑制效果与边缘、点目标信息的保持能力却有待提高。针对这一问题,提出了一种基于非负特征值分解(NNED)的极化SAR子空间分解滤波。对于每一个像素点,首先计算其参数向量协方差矩阵的特征值与特征向量,进而得到各个特征子空间;然后,以散射机制相似度最小化为标准,利用NNED选取分离信号子空间与噪声子空间的最优阈值;最后根据信号子空间得到滤波后的结果。实测极化SAR实验表明,相比于同类算法,所提出的算法能有效地抑制斑点噪声并且能很好地保持边缘、点目标信息。     

基于ULV更新算法的DS-CDMA自适应检测研究

通过研究多径信号码空间和数据矢量空间,采用噪声子空间技术进行异步DS-CDMA系统期望信号矢量估计,以利于把盲线性滤波优化技术应用于稳健的干扰抑制。提出一种修改的ULV更新算法进行噪声子空间跟踪,该算法不需要相关矩阵的秩估计,直接估计噪声子空间,不进行信号子空间跟踪。仿真结果验证了该文算法的有效性。     

基于最小统计噪声估计的信号子空间语音增强

针对传统子空间方法中，采用语音活动检测（Voice activity detection，VAD）估计噪声的缺陷，提出了一种基于子空间域的最小统计噪声估计算法。噪声估计通过跟踪带噪语音协方差矩阵用每个特征向量上的特征值的最小值来获得，该方法不需要VAD明确区分语音段和噪声段，能够在整个信号期间实现噪声的连续估计和不断更新。实验结果表明，相对于传统的基于VAD的子空间方法，本文提出的算法对语音增强效果有非常显著的提高。     

子空间盲辨识的改进算法

针对单输入多输出(SIMO)系统模型参数的盲辨识问题进行了研究,基于二阶统计量,提出一类改进的子空间辨识算法.依据协方差阵的秩对该矩阵进行分块,在此基础上考虑了实际系统中存在的噪声误差,利用总体最小二乘(TLS)得到一个与噪声子空间相关的量,最后对该量进行标准正交化,得到了噪声子空间.与传统子空间方法相比,改进算法不需要对协方差矩阵进行特征值分解,可以减弱噪声及不确定因素的影响,减少了运算量,仿真实验结果表明了该算法的有效性.     

相干信号的空间谱估计算法研究

研究相干信号谱估计精度问题,由于空间信号噪声影响,识别精度低。为此提出了一种改进的解相干谱估计算法——全局滑动修正算法。可通过对平滑子阵划分的修正,突破了阵列布局限制,有效地使可利用的子阵数目大于双向空间平滑中的子阵个数,采用全部的阵元当做一个滑动的子阵,得到一个新的包含空间谱信息的协方差矩阵,再对其进行特征值分解,然后按照MUSIC算法进行谱估计。仿真结果证明,全局滑动修正算法能很好地对相干信号进行解相干DOA估计,并且在信噪比较低时,比前后向空间平滑算法具有更高的估计精度。     

基于子空间逼近的特征空间波束形成算法

特征空间波束形成（ESB）算法为了得到信号子空间需要对采样协方差矩阵进行特征值分解,运算量十分巨大,这大大限制了其应用。为了减低ESB算法的运算量,利用有理子空间逼近的原理,提出一种不需要估计信号源个数的快速ESB算法。该方法利用一个介于信号和噪声特征值之间的分界值将特征空间分成两个子空间,并用矩阵幂乘和此分界值的有理式逼近这两个子空间的投影矩阵,将此投影矩阵代入到ESB算法的权值求解式中,在不降低性能的前提下,可大大提高波束形成的运算速度。计算机仿真验证了该算法的有效性,并分析了分界值取值方法的不同对子空间划分及波束形成性能的影响。     

宽带距离深度域最小二乘矩阵滤波器的设计

设计出基于拷贝向量处理的适用于宽带信号滤波的距离深度域矩阵滤波器。提出利用最小二乘原理将矩阵滤波器的设计问题转化为超定线性方程组求解问题,通过对线性方程组最小二乘解化简重构,得出最小二乘矩阵滤波器为期望拷贝向量矩阵与实际拷贝向量矩阵右伪逆的乘积。通过对宽带信号频谱中能量较高的频点进行采样处理,可以将矩阵滤波器应用于宽带信号处理。分别对垂直均匀线列阵接收到的窄带和宽带数据进行处理,发现宽带矩阵滤波器可以保证水听器阵在恶劣环境中更加有效地工作。相比于二阶锥规划矩阵滤波器,距离深度域最小二乘矩阵滤波器更快捷,滤波之后目标源与干扰源的相对幅度更大,有利于目标检测。     

Statistical Estimation and Classification on Commutative Covariance Structures

Statistical inference is investigated under the following constraints on the covariance structure for the observation vector: covariance matrices belong to some commutative matrix algebra. Commutative approximation of arbitrary covariance structures and statistical estimation of the parameters of a given commutative structure are studied. The results are applied to statistical classification of Gaussian vectors having commutative covariance structure.     

Linear filtering with adaptive adjustment of the disturbance covariation matrices in the plant and measurement noise

For filtering a nonstationary linear plant under the unknown intensities of input signals such as plant disturbances and measurement noise, a new algorithm was presented. It is based on selecting the vectors of values of these signals compatible with the observed plant output and minimizing the error variances of the last predicted measurement. The measurement prediction is determined from the Kalman filter where the input signals are assumed to be white noise and the covariance matrix coincides with the empirical covariance matrix of the selected vectors. Numerical modeling demonstrated that the so-calculated filter coefficients are close to the optimal ones constructed from the true covariance matrices of plant disturbances and measurement noise. The approximate Newton method for minimization of the prediction error variance was shown to agree with the solution of the auxiliary optimal control problem, which allows to make one or some few iterations to find the point of minimum.     

Cross grouping strategy based 2DPCA method for face recognition

Grouping strategy exactly specifies the form of covariance matrix, therefore it is very essential. Most 2DPCA methods use the original 2D image matrices to form the covariance matrix which actually means that the strategy is to group the random variables by row or column of the input image. Because of their grouping strategies these methods have two main drawbacks. Firstly, 2DPCA and some of its variants such as A2DPCA, DiaPCA and MatPCA preserve only the covariance information between the elements of these groups. This directly implies that 2DPCA and these variants eliminate some covariance information while PCA preserves such information that can be useful for recognition. Secondly, all the existing methods suffer from the relatively high intra-group correlation, since the random variables in a row, column, or a block are closely located and highly correlated. To overcome such drawbacks we propose a novel grouping strategy named cross grouping strategy. The algorithm focuses on reducing the redundancy among the row and the column vectors of the image matrix. While doing this the algorithm completely preserves the covariance information of PCA between local geometric structures in the image matrix which is partially maintained in 2DPCA and its variants. And also in the proposed study intra-group correlation is weak according to the 2DPCA and its variants because the random variables spread over the whole face image. These make the proposed algorithm superior to 2DPCA and its variants. In order to achieve this, image cross-covariance matrix is calculated from the summation of the outer products of the column and the row vectors of all images. The singular value decomposition (SVD) is then applied to the image cross-covariance matrix. The right and the left singular vectors of SVD of the image cross-covariance matrix are used as the optimal projective vectors. Further in order to reduce the dimension LDA is applied on the feature space of the proposed method that is proposed method + LDA. The exhaustive experimental results demonstrate that proposed grouping strategy for 2DPCA is superior to 2DPCA, its specified variants and PCA, and proposed method outperforms bi-directional PCA + LDA.     

一种基于协方差阵拟合的解测向模糊算法

流形模糊发生的原因是阵列流形上的多个矢量线性相关,子空间类算法无法靠自身解模糊。为解决子空间类算法中的模糊问题,提出了一种新的基于协方差阵拟合的解模糊算法。该方法用预估的导向矢量与入射波功率来拟合协方差阵,用最陡下降法寻找最优的功率参数,认定功率较大的入射波为真实来波。与子空间算法结合,可解决测向模糊问题。数值仿真表明,此方法在阵元数量较多时仍能有效工作。     

Wiener系统中线性函数辨识的渐近性分析

针对Wiener系统中的两类未知参数以相互结合的形式出现在非线性函数中,通过预测误差法辨识此两类未知参数,进而确定Wiener系统中线性部分的系统对象模型的渐近方差矩阵形式。在白噪声激励的作用下,推导出Wiener系统中线性部分的渐近方差表达式。此渐近方差表达式中不包含有模型阶数的存在,其利用某个由正交基构成的生成核函数来替换原模型阶数,使得在已知某些先验信息知识的前提下,该渐近方差式能更精确地接近于各自对应的真实采样值。最后用仿真算例验证本文方法的有效性和可行性。     

Covariance matrix estimation for left-censored data

Multivariate methods often rely on a sample covariance matrix. The conventional estimators of a covariance matrix require complete data vectors on all subjects—an assumption that can frequently not be met. For example, in many fields of life sciences that are utilizing modern measuring technology, such as mass spectrometry, left-censored values caused by denoising the data are a commonplace phenomena. Left-censored values are low-level concentrations that are considered too imprecise to be reported as a single number but known to exist somewhere between zero and the laboratory’s lower limit of detection. Maximum likelihood-based covariance matrix estimators that allow the presence of the left-censored values without substituting them with a constant or ignoring them completely are considered. The presented estimators efficiently use all the information available and thus, based on simulation studies, produce the least biased estimates compared to often used competing estimators. As the genuine maximum likelihood estimate can be solved fast only in low dimensions, it is suggested to estimate the covariance matrix element-wise and then adjust the resulting covariance matrix to achieve positive semi-definiteness. It is shown that the new approach succeeds in decreasing the computation times substantially and still produces accurate estimates. Finally, as an example, a left-censored data set of toxic chemicals is explored.     

非高斯杂波协方差矩阵估计方法及CFAR特性分析

在独立同分布的球不变随机向量非高斯杂波背景下,针对采样协方差矩阵(SCM)、归一化采样协方差矩阵(NSCM)及相应的迭代估计矩阵(NSCM-RE),利用统一理论框架分析了3种不同估计矩阵对应的自适应归一化匹配滤波器(ANMF)的恒虚警率(CFAR)特性.理论分析表明,SCM-ANMF只对杂波归一化协方差矩阵具有CFAR特性;而NSCM-ANMF只对杂波功率水平具有CFAR特性;在有限次迭代下,NSCM-RE-ANMF对杂波归一化协方差矩阵不具有CFAR特性.为了改善ANMF的自适应特性,提出了一种自适应协方差矩阵估计方法(AE),并将其作为初始化矩阵进行迭代估计,在有限次迭代下,所获得的AE-RE-ANMF对杂波归一化协方差矩阵和功率水平均具有CFAR特性.最后,通过仿真验证了所提方法的有效性.     

Local fuzzy PCA based GMM with dimension reduction on speaker identification

To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix on each cluster. Finally, the GMM for speaker is obtained from the transformed feature vectors with reduced dimension in each cluster. Compared to the conventional GMM with diagonal covariance matrix, the proposed method shows faster result with less storage maintaining same performance.