单矢量水听器方位频率联合估计新方法

旋转不变子空间法和多重信号分类法需假设背景噪声为独立的高斯白噪声或自相关矩阵已知,当条件不满足时算法的性能急剧下降。针对这一问题,根据矢量水听器多通道输出的特点,提出了一种基于平行因子模型的单矢量水听器方位频率联合估计方法。首先利用矢量水听器各个通道t时刻和t+1时刻的输出数据,计算声压和各振速不同组合时的四阶累积量,并构建三阶平行因子模型;然后分析了 PARAFAC 模型低秩分解的唯一性条件并利用三线性交替最小二乘算法得到了单矢量水听器阵列流形和相位延迟估计,进而得到目标的方位和频率估计。与旋转不变子空间法和多重信号分类法相比,该方法不需要子空间估计和谱峰搜索,在高斯噪声和拉普拉斯噪声背景下对多目标的分辨能力好于ESPRIT算法。仿真和实测数据的分析结果证明了算法的有效性。     

基于均匀圆阵的信号二维方向角和多普勒频率的盲估计

针对均匀圆形阵列 ,在时空旋转因子的基础上 ,推导出四阶累积量矩阵 ,采用波达方向矩阵法 ,实现了信号的方位角、俯仰角和多普勒频率的同时估计。此估计算法利用所有阵元信息估计信号波达方向 ,提高了方向估计精度 ,解决了“频率兼并”问题 ,使有效应用范围扩大。利用了四阶累积量的盲高斯性 ,有效地抑制了高斯白或有色噪声的影响 ,仿真结果表明了此算法的有效性     

基于空时相关阵联合对角化的子空间DOA估计

为提高空间相关噪声场中的目标方位估计性能,提出一种基于空时相关阵联合块对角化的子空间方位估计算法.具体利用Jacobi旋转矩阵法对一组空时相关阵联合近似对角化,用联合对角化特征向量矩阵和特征值修正MUSIC(Multiple Signal Classification)等子空间算法.理论和仿真结果表明,在非相关噪声场中,基于联合对角化的子空间算法性能与常规子空间算法基本一致;而在相关噪声场中,联合对角化特征向量法能显著减小方位估计方差,提高估计性能.     

利用二阶循环平稳性的信道自适应盲辨识

提出了利用二阶循环平稳性信道盲辨识的自适应求解方法。通过递推更新循环相关函数,从而完成了循环相关矩阵的自适应估计。利用共轭梯度搜索的方法实现信道的自适应盲辨识,并讨论了梯度算法中步长选取的问题,提出了可变步长选取的方法。由于采用二阶循环统计量,因此算法既简单又可以很好的抑制平稳加性有色噪声。计算机仿真表明,在低信噪比情况下,自适应算法能很好的工作。     

基于加权四阶累积量切片的谱线增强

本文首先介绍了加权高阶累积量切片的概念,给出了加权混合高阶累积量的更新公式,提出了其自适应谱线增强算法,并用实测运动目标辐射噪声数据,对该算法的性能进行了仿真研究。仿真结果表明:该算法具有较强的抑制高斯有色噪声能力,能抑制大约13~23dB的高斯有色噪声;调整高阶累积量切片加权系数,可改善该算法抑制高斯有色噪声的性能。     

空间非平稳噪声下圆阵的修正Capon算法

在空间非平稳噪声环境下,利用估计的噪声相关矩阵对圆阵接收数据相关矩阵进行预处理,可以消除非平稳噪声对方位估计的影响。再利用修正Capon算法,可以突破瑞利限的限制,且不需要知道信源数,从而实现目标的高分辨方位估计。仿真结果证实了该方法的有效性。     

一种改进的WSF算法在单矢量水听器多目标方位估计中的应用

文章针对单个矢量水听器的多目标方位估计,提出了一种基于加权子空间拟合(WSF)的算法,该算法首先对单个矢量水听器接收数据作一任意的时间延迟,而后仿照ESPRIT算法的思路求解阵列响应矩阵,从中抽取各目标的波达方位。该算法在保留WSF算法分辨力高、估计方差小的优点的同时,通过结合ESPRIT算法的思想,克服了WSF算法计算量大,需迭代求解的缺点。由于该算法和声源频率无关,因而可直接应用于宽带声源的测向,并避免了传统ESPRIT算法中因估计延时相位而导致的频率-方位模糊问题。文中通过数值仿真和推导Cramer-Rao下界,给出了该算法的性能评价,数值仿真和湖试实验结果也充分验证了该算法的有效性。     

基于三阶累积量及自适应滤波时延估计的管道定位方法

针对直接互相关被动时延估计法定位管道异常振动事件存在噪声干扰影响定位精度的问题,提出了基于三阶累积量及自适应滤波时延估计的管道异常事件定位方法。该方法对顺、反两路异常振动信号进行三阶自累积量和互累积量估计,抑制高斯相关噪声和对称分布噪声。然后利用自适应滤波时延估计算法对三阶自累积量和互累积量信号的时延进行迭代计算,在不依赖先验知识的情况下抑制非高斯相关噪声。经现场实验证明,该方法可以准确地对管道异常事件进行定位,对噪声具有很好的抑制作用,改善了直接互相关时延估计的性能。相对于直接互相关时延估计方法,相对定位误差由2.7%降低到0.6%,定位一致性提高了三倍,平均定位精度可达14m。     

一种基于数据矩阵的快速ESPRIT实现

本文首先介绍了ESPRIT算法的基本原理,在此基础上提出了一种新的直接基于数据矩阵的D-ESPRIT方法,该方法利用高斯白噪声均值为零的特性来消除混合在有效信号中的噪声,通过构造新的矩阵束求解广义特征值进一步获取正弦组合信号的成分频率.与基本ESPRIT算法相比,该方法可以大大节省计算量,加快求解速度,而性能接近.计算机仿真证实了方法的有效性.     

基于协方差扩展PM算法的波达方向估计

针对基于传播算子方法(Propagator Method, PM)的水听器阵波达方向(Direction of Arrival, DOA)估计在低信噪比或者小快拍数时性能变差的问题,文章提出一种改进的基于PM算法的水听器阵方位估计方法。该方法利用信号子空间的旋转不变性特征对协方差矩阵进行扩展和重构,通过分块协方差矩阵的子矩阵得到传播算子矩阵。通过传播算子矩阵构造扩展噪声子空间,然后利用信号子空间与噪声子空间的正交性估计空间谱。仿真实验和湖上实验的结果表明：相较于传统PM方位估计算法,文中算法在低信噪比或者小快拍情况下具有较好的方位估计性能,在信噪比为0 dB时,文中方法比传统PM算法均方根误差减少0.6°;在快拍数为150时,比传统PM算法的均方根误差减少0.1°。     

一种均匀线阵互耦校正算法

旋转不变子空间(Estimating Signal Parameters via Rotational Invariance Techniques,ESPRIT)算法是空间谱估计中的典型算法,但是阵列互耦会严重影响ESPRIT算法的测向性能。将均匀线阵划分为冗余阵元和有效中心阵元,对有效中心阵元利用ESPRIT算法估计出校正源方位角,结合冗余阵元信息估计出互耦系数阵。计算机仿真显示该算法在互耦自由度为2或3时均有效,算法还从仿真角度研究了幅度和相位误差对算法性能的影响。该算法校正时只需单个未知方位校正源,是一种操作简单的均匀线阵互耦校正算法。     

Direction of arrival estimation for uniform circular array based on fourth-order cumulants in the presence of unknown mutual coupling

A direction of arrival (DOA) estimation algorithm in the presence of an unknown mutual coupling is presented for a uniform circular array. This algorithm is based on the fourth-order cumulants, and the DOA of signal sources can be accurately estimated without the need of any calibration source since the coupling is blindly compensated by the inherent mechanism of the proposed method. Because of the use of higher-order statistics, the number of sources that can be coped with may be larger than the number of sensors in the array, and the algorithm is insensitive to Gaussian noise. Comparing with existing calibration methods which use iterative approaches, this algorithm is computationally less expensive since it uses only a one-dimensional search. Validation and performance are illustrated by simulations.     

Generalized Array Architecture with Multiple Sub-Arrays and Hole-Repair Algorithm for DOA Estimation

Arranging multiple identical sub-arrays in a special way can enhance degrees of freedom (DOFs) and obtain a hole-free difference co-array (DCA). In this paper, by adjusting the interval of adjacent sub-arrays, a kind of generalized array architecture with larger aperture is proposed. Although some holes may exist in the DCA of the proposed array, they are distributed uniformly. Utilizing the partial continuity of the DCA, an extended covariance matrix can be constructed. Singular value decomposition (SVD) is used to obtain an extended signal sub-space, by which the direction-of-arrival (DOA) estimation algorithm for quasi-stationary signals is given. In order to eliminating angle ambiguity caused by the holes of DCA, the estimation of signal parameters via rotational invariance techniques (ESPRIT) is used to construct a matrix that includes all angle information. Utilizing this matrix, a secondary extended signal sub-space can be obtained. This signal sub-space is corresponding to a hole-free DCA. Then, dealing with the further extended signal sub-space by multiple signal classification (MUSIC) algorithm, the unambiguous DOAs of all incident signals can be estimated. Some simulation results are shown to prove the improved performance of proposed generalized array architecture in DOA estimation and the effectiveness of corresponding hole-repair algorithm in eliminating angle ambiguity.     

语音模糊消噪算法

针对加性有色噪声,提出了语音信号模糊消噪算法;建立并训练了一个语音模糊消噪系统——自适应神经模糊推理系统（ANFIS）;用其对含噪语音中的有色噪声进行模糊估计,从而提取出干净的语音。对算法进行了仿真实验,结果表明,对模拟有色噪声在-17dB时能提取出清晰的语音。     

低信噪比下重构协方差矩阵的高分辨MUSIC算法

在满足对称分布的海洋噪声场中,为提高低信噪比条件下目标方位估计性能,提出一种重构信号协方差矩阵的MUSIC算法。利用数据协方差矩阵虚部与对称噪声无关的性质,根据协方差矩阵虚部和虚部MUSIC算法的预估角重构出信号协方差矩阵,在此基础上实现MUSIC算法。仿真结果表明,所提算法相比常规MUSIC算法能有效降低对称噪声的影响,提高方位估计性能,并避免双边谱的出现,有更高的分辨率和更低的分辨门限。还研究了协方差矩阵的Toeplitz修正处理对于MUSIC类算法的改善作用。仿真表明,Toeplitz修正处理能显著提高MUSIC类算法的分辨性能。     

谐波恢复的时间平均三阶累积量方法及其工程应用

为了识别在强高斯噪声背景下振动信号的谐波成分，从三阶累积量的估计算法出发，提出了基于时间平均的三阶累积量算法，进行振动信号的谐波恢复。时间平均三阶累积量是三阶累积量的一种估计值。理论推导表明，随机相位谐波过程时间平均三阶累积量为非零值，而且其一维切片仍然是谐波过程。由此提出了一种在强高斯噪声背景下识别信号的谐波成分的频谱分析方法。该方法对抑制振动信号中的高斯噪声、正确识别其中的谐波成分十分有效，工程应用实例和信号仿真都很好地验证了该方法的正确性。     

On the sensitivity of the ESPRIT algorithm to non-identical subarrays

ESPRIT (estimation of signal parameters via rotational invariance techniques) is a recently introduced algorithm for narrowband direction-of-arrival (DOA) estimation. Its principal advantage is that the DOA parameter estimates are obtained directly, without knowledge (and hence storage) of the array manifold and without computation or search of some spectral measure. This advantage is achieved by constraining the sensor array to be composed of two identical, translationally invariant subarrays. In this paper, we analyse the sensitivity of ESPRIT to the assumption that the subarrays are identical. The analysis is applicable to a wide variety of array errors, including non-identical angle-dependent and angle-independent gain and phase perturbations, errors in the locations of the subarray elements, and mutual coupling effects. A representative simulation example will be presented to validate the analysis and compare the performance degradation of ESPRIT with that of the MUSIC algorithm.     

DOA estimation for non-Gaussian signals using fourth-order cumulants

A novel method based on fourth-order cumulants (FOC) is proposed for direction of arrival (DOA) estimation with uniform linear array (ULA). The method can be applied in the situation that the non-Gaussian independent and coherent signals coexist with unknown coloured Gaussian noise. The method comprises two steps: the first step is to estimate the independent signals, and then they are eliminated; the second step is to resolve the coherent signals with the reconstructed FOC matrix of coherent signals. The proposed method can also be extended to the scenario when independent, partially correlated and coherent signals coexist, and the number of signals resolved by our method can exceed the number of array elements. Simulation results demonstrate the effectiveness and efficiency of our method.     

分布式传声器阵列的低频宽带信号方位估计

现有的预防道路交通安全事故、治理道路交通噪声污染等问题的解决方案是从视觉维度监控重点区域并通过声音维度确定事件触发类型与位置。为了实现公路异常声源的实时监测,提出了一种基于双尺度旋转不变信号参数估计旋转不变子空间技术(Estimation of Signal Parameters via Rotational Invariance Techniques, ESPRIT)的低频宽带声源波达方向(Direction of Arrival, DOA)估计算法,该算法适用于三个矩形子阵呈三角形分布的分布式阵列。算法利用该分布式阵列具有的子阵内相邻阵元间距、相邻子阵间距两种尺度对应的空间平移不变性分别进行方向余弦估计,并利用基于阵型分布的解模糊策略实现高精度方位估计。仿真结果验证了算法的有效性,表明了基于该算法的分布式阵列DOA估计精度优于相同阵元数与阵元间距的单个均匀矩形阵,分析了估计精度与分布基线长度的关系,体现了算法的实际工程应用价值。     

Optimal multichannel estimation of the location of a target with nonoverlapping noise

We propose a new algorithm for estimating the location of an object in multichannel images when the noise is spatially disjointed from (nonoverlapping with) the target. This algorithm is optimal for nonoverlapping noise and for multichannel images in the maximum-likelihood sense. We consider the case in which the statistical parameters of the input scene are unknown and are estimated by observation. We assess the results for simulated images with white and Gaussian background, for a large scale of variances of the background noise, and different values of the contrast in the scene. We compare the results of this algorithm with the results obtained with two other algorithms, the optimal algorithm for monochannel nonoverlapping noise and the optimal algorithm for multichannel additive noise, and we show that in both cases improvement can be obtained. We show the efficiency of the estimation for real input scenes when the background noise is correlated clutter noise. This algorithm has the same complexity as correlation, and the improvement is obtained with no more calculation cost than with classic methods.