Khatri-Rao积变换下的离格信号DOA估计

当存在离格信号时,基于稀疏表示的波达角(DOA)估计算法性能损失严重.为解决这个问题,在对接收数据协方差矩阵进行Khatri-Rao积变换的基础上,推导了离格信号网格偏离量与紧邻信号原子系数之间的关系,提出了一种单一离格信号DOA估计方法.为提高对邻近离格信号DOA的估计性能,利用矩阵的广义逆性质提出了基于多原子系数的联合估计方法.仿真实验表明,单一离格信号DOA估计方法在低信噪比下有较好的性能,联合估计方法在高信噪比条件下对邻近离格信号DOA有较高的估计精度,同时所提算法估计性能几乎不受网格划分间距的影响,可以通过增大网格间距降低算法运算量.相关研究对阵列天线DOA估计具有一定的参考价值.     

互质阵列DOA估计性能综合分析北大核心CSCD

互质阵列是近年来兴起的新型阵列,能显著提高阵列自由度,处理信源数大于阵元数时的波达方向(DOA)估计,且能提高角度分辨率和测角精度。文中根据互质阵物理阵元和虚拟阵元特点,结合多重信号分类(MUSIC)算法提出适用于互质阵基于物理阵列和虚拟阵列的DOA估计方法。该方法以非相干信号源为研究对象,利用互质阵列建立信号接收模型,基于物理阵列的DOA估计方法根据互质阵物理阵元位置特点推导其导向矢量,然后根据导向矢量计算回波信号数据和信号协方差矩阵,最后利用MUSIC算法进行DOA估计。基于虚拟阵列的DOA估计方法根据其虚拟阵元数据特点在向量化协方差矩阵并去冗余后选取连续虚拟阵元接收数据,然后对新协方差矩阵进行一维Toeplitz平滑重构,最后利用MUSIC算法或求根MUSIC算法进行DOA估计。与等阵元数的均匀线阵进行对比,仿真实验验证了互质阵列DOA估计性能的优越性。     

基于虚拟阵列空间平滑的相干信号DOA估计

 针对相干共轭循环平稳信号的DOA估计问题,提出了一种基于虚拟阵列前后向空间平滑的方法。该方法利用特定阵元输出间共轭循环相关函数的多时延采样,构造出与真实线阵阵元具有一一对应关系的虚拟阵列,并将空间平滑技术应用于该虚拟阵列实现了相干信号DOA估计。理论分析和仿真结果均表明,与FBSS-CCMUSIC算法和CCHAM算法相比,本文方法避免了最优时延选择问题,并获得了更高的DOA估计精度,同时,对信号不同入射方向也具有较好的稳健性。此外,该方法也可推广用于具有循环平稳特性的信号。     

相干语音信号的两阵元DOA估计新算法北大核心CSCD

针对语音信号非平稳性、非调制性及高分数带宽比的特点,提出一种新的基于两阵元的相干语音信号DOA估计算法。该算法先从接收信号中选出低频部分能量较大且功率谱分布较均匀的帧为最优帧,并分解为互不重叠的窄带分量,利用希尔伯特变换将各窄带转换成解析信号,将信号的不同频率等效为虚拟阵列的各个阵元,对虚拟阵列接收信号去相干处理后进行DOA估计。本文从信源个数、划分窄带数、信噪比等方面对提出的算法进行仿真实验研究,仿真结果及分析表明该算法对高信噪比的相干语音信号DOA估计具有一定的适用性,克服了两阵元只能估计单声源的不足,实现了多个相干声源的DOA估计。     

基于虚拟阵列的非圆信号DOA估计

针对传统的子空间类波达方向（Direction of Arrival,DOA）估计算法只适用于入射信号个数少于天线数的局限性,利用现代通信系统中常用的非圆信号实值特性,提出了一种虚拟阵列多重信号分类法（Virtual Array Based Multiple Signal Classification,VA-MUSIC）。该方法通过对阵列输出信号进行共轭重构和合并,获得虚拟阵列来增加阵列的有效孔径。更进一步,结合空间平滑技术有效地解决了相干信号的DOA估计问题。与传统的MUSIC算法相比,新算法不仅可以增加最大可估计信源数,而且在DOA估计精度、信号源角分辨能力等方面均有明显的优势。计算机仿真验证了该算法的有效性和优越性。     

稀疏极化敏感阵列的波达方向和极化参数联合估计

该文采用稀疏分布极化敏感阵列(SD-PSA),研究了多目标波达方向(DOA)和极化参数的估计问题。首先建立稀疏极化敏感阵列信号模型;然后利用阵列的空间旋转不变性运用ESPRIT算法得出信号的高精度周期性模糊多值DOA估计;同时利用子阵列导向矢量之间的关系得出信号的极化信息和DOA的无模糊粗估计;最后利用DOA粗估计值解模糊,得到信号的高精度无模糊DOA估计。该文所提阵列的阵元间距大于半个波长距离,扩展了阵列2维物理孔径,一定程度上降低了阵元间的互耦影响,相应的信号DOA估计精度大大提高。仿真实验结果验证了该算法对信号DOA和极化参数估计的有效性。     

基于最优子阵划分ESPRIT的任意线阵高精度DOA估计算法

为了提高任意阵列的波达方向（direction of arrival, DOA）估计性能,从对子阵阵元选取进行优化的角度出发,提出了基于最优子阵划分旋转不变信号参数估计技术(estimation of signal parameters via rotational invariance techniques, ESPRIT)的任意线阵高精度DOA估计算法. 该算法首先利用虚拟插值阵列ESPRIT（virtual interpolated array ESPRIT, VIA-ESPRIT）得到精度较低的DOA粗估计. 其次以DOA粗估计为参考对任意阵列进行相位补偿,使其具备旋转不变性. 然后根据ESPRIT算法原理对构建旋转不变方程的子阵划分进行优化,并通过优化后子阵间的旋转不变性得到高精度的DOA估计. 此外,本文还分析了子阵划分对算法估计性能的影响,给出了子阵最优选取的近似计算方法. 计算机仿真结果验证了所提算法的有效性,并表明其性能逼近克拉美·罗界（Cramer-Rao bound, CRB）.     

基于斜投影的DOA估计和盲信号识别协同算法

阵列信号处理领域中,经常需要分析阵列接收信号的波达方向(DOA)和波形信息。在缺乏信号先验DOA和波形信息的情况下,提出了一种新的斜投影DOA估计和盲信号识别协同算法,实现对未知阵列接收信号的自适应分析。该算法交替地采用斜投影空域滤波技术将阵列接收信号拆分为单信号群组,在每个单信号群组内进行DOA和波形联合估计。算法适用于主瓣干扰存在的情况,并且在中小快拍数下有较好的估计性能。理论分析和仿真实验证实了文中算法的有效性。     

智能天线中时空虚拟DOA矩阵算法与改进

本文主要研究智能天线中二维DOA估计算法.首先研究时空DOA矩阵估计方法,并进行了仿真.然后结合虚拟阵列变换技术对该算法进行有效的改进一时空虚拟DOA矩阵方法.改进后得到的仿真结果证明了该改进算法可以估计超过阵元数的信号,具有较高的分辨性能和较好的稳健性.     

基于2D-Unitary ESPRIT算法的DOA估计新方法

提出了一种基于2D-Unitary ESPRIT算法的六角星形阵列波达方向(DOA)估计方案。利用星形阵列子阵列的多种平移不变性,将子阵列两两组合,采用2D-Unitary ESPRIT算法分别对每种组合进行估计,获取了DOA估计的候选集,然后运用2D-MUSIC算法的功率谱函数在候选集中对DOA进行最优估计,得出DOA的最优估值,并进行了仿真实验。该方案避免了由2D-MUSIC算法因谱峰搜索而带来的庞大计算成本,且多种平移不变子阵列组合的运用减小了DOA估计误差。仿真表明,此方案与传统2D-Unitary ESPRIT算法相比,减小了估计值的均方根误差,提高了空间分辨率。     

相关噪声条件下SUMPLE算法权值估计方法

天线组阵技术是提高深空探测微弱信号信噪比的有效方法。随着大规模天线组阵中单个天线接收信号信噪比的降低,来自背景天体的相关噪声的影响不容忽视。针对相关噪声对天线组阵SUMPLE算法权值估计的影响进行了分析,给出了一种基于频域的抑制相关噪声影响的处理方法。计算机仿真结果表明:在相关噪声背景下,该方法能有效地提高SUMPLE算法权值相位的估计精度,抑制相关噪声的干扰。     

基于传声器阵列语音增强方法研究

传声器阵列通过对拾取的多路语音信号进行分析与处理,能取得改进语音质量、消除背景噪声和提高语音可懂度等明显效果,现已成为语音信号增强的一个重要的研究领域。介绍了基于传声器阵列的自适应波束形成方法,该方法采用GSC结构基于TF-GSC的最优后置滤波算法。仿真实验结果表明,该自适应波束形成器对干扰有很好的消除作用,对阵元的增益误差、位置误差不敏感,可以取得较好的语音增强效果。     

基于均值漂移的自适应滤波及其在光谱信号处理中的应用

该文给出了一种基于均值漂移的自适应双边滤波方法,其性能仅取决于空域的核尺度参数,幅度域的核尺度是根据信号的局部特征自适应选取的。该方法能够去除脉冲噪声,能有效抑制非脉冲噪声,并有较强的边缘保护能力。实验和分析表明本文方法的整体性能优于高斯滤波和中值滤波。该文将所提出方法用于天体光谱的去噪,并与均值漂移滤波和小波硬阈值法进行了比较,结果表明:该方法能够有效抑制光谱中天光背景噪声和随机噪声,并能较好地保护谱线信息,更适于天体光谱信号的处理。     

An algorithm for linearly constrained adaptive array processing

A constrained least mean-squares algorithm has been derived which is capable of adjusting an array of sensors in real time to respond to a signal coming from a desired direction while discriminating against noises coming from other directions. Analysis and computer simulations confirm that the algorithm is able to iteratively adapt variable weights on the taps of the sensor array to minimize noise power in the array output. A set of linear equality constraints on the weights maintains a chosen frequency characteristic for the array in the direction of interest. The array problem would be a classical constrained least-mean-squares problem except that the signal and noise statistics are assumed unknown a priori. A geometrical presentation shows that the algorithm is able to maintain the constraints and prevent the accumulation of quantization errors in a digital implementation.     

Frequency tracking techniques of power systems in coloured noises

A technique for tracking the frequency of power systems in sine noises using numerical differentiation is presented. A voltage or current sinusoidal signal corrupted by sine noises and white noises is considered. For the signal corrupted by one or two sine noises, a central numerical differentiation-based method is proposed. For the signal corrupted by multiple sine noises and white noises, a hybrid method of numerical differentiation and a digital finite impulse response (FIR) filter are proposed. The digital FIR filtering algorithm is used to remove the white noises and the sine noises, and the numerical differentiation algorithm is used to estimate the fundamental frequency of power systems when the fundamental component is decomposed out of the signal. The proposed algorithm shows an advantage in time and speed when compared with other existing techniques and shows better dynamics and higher accuracy in frequency estimation. Carried out in Matlab, the simulation results are satisfactory     

Adaptive antenna systems

A system consisting of an antenna array and an adaptive processor can perform filtering in both the space and the frequency domains, thus reducing the sensitivity of the signal-receiving system to interfering directional noise sources. Variable weights of a signal processor can be automatically adjusted by a simple adaptive technique based on the least-mean-squares (LMS) algorithm. During the adaptive process an injected pilot signal simulates a received signal from a desired "look" direction. This allows the array to be "trained" so that its directivity pattern has a main lobe in the previously specified look direction. At the same time, the array processing system can reject any incident noises, whose directions of propagation are different from the desired look direction, by forming appropriate nulls in the antenna directivity pattern. The array adapts itself to form a main lobe, with its direction and bandwidth determined by the pilot signal, and to reject signals or noises occurring outside the main lobe as well as possible in the minimum mean-square error sense. Several examples illustrate the convergence of the LMS adaptation procedure toward the corresponding Wiener optimum solutions. Rates of adaptation and misadjustments of the solutions are predicted theoretically and checked experimentally. Substantial reductions in noise reception are demonstrated in computer-simulated experiments. The techniques described are applicable to signal-receiving arrays for use over a wide range of frequencies.     

基于均圆阵的扩展循环 MUSIC 算法简

将循环平稳理论引入到均匀圆阵的波达方向估计中,提出了一种基于圆阵的扩展循环MUSIC算法。同时利用信号的循环相关阵和循环共轭相关阵的信息,有效抑制了同频带内干扰和噪声的影响,实现了感兴趣信号与干扰信号的有效分离。仿真实验结果表明,所提算法具有较高的波达方向估计精度和较好的多信号分辨能力,且突破了经典MUSIC算法关于信源数不能超过阵元数的限制。     

一种混合噪声图像的自适应滤除方法

基于小波变换的阈值去噪方法仅适用于去除高斯白噪声,对于脉冲噪声得不到好的去噪效果,正交小波变换由于缺乏平移不变性,在去噪过程中会产生人为的振荡现象,使图像边缘失真,甚至图像模糊,提出了基于平稳小波域自适应阈值算法同中值滤波相结合的去噪方法,该方法能有效地滤除图像中的高斯白噪声和脉冲噪声组成的混合噪声,并验证了该方法的有效性。     

Filtering noncorrelated noise in impedance cardiography

Impedance cardiography (ICG) may be altered by noises as respiration and movement artifacts, mainly during exercise. In this work, a scaled Fourier linear combiner (SFLC) event-related to the R-R interval of ECG is proposed. It estimates the deterministic component of the impedance cardiographic signal and removes the noises uncorrelated to this interval. The impedance cardiographic signal is modeled as Fourier series with the coefficients estimated by the least mean square (LMS) algorithm. Simulations have been carried out to evaluate the filter performance for different noise conditions. Moreover, the method capability to remove uncorrelated noises was also examined in physiological data obtained in rest and exercise, by synchronizing respiration and pedalling with a metronome. Analyzing the ICG power spectrum, it was concluded that the proposed filter could remove the noises that are not synchronized with heart rate     

非平稳卷积混合信号的盲分离

传感器采集到的信号是由多目标源、环境噪声等经多途径卷积混合的形武.为有效地去除环境因素如干扰、传输延迟等的影响,提出一种新的盲信号分离方法.利用非平稳信号的多重去相关和最小二乘准则来估计混合矩阵A或解混矩阵W以及信号和噪声功率.实验结果表明,该算法具有良好的分离效果.