2D DOA Estimation of Coherent Signals with a Separated Linear Acoustic Vector-Sensor Array

In this paper, a two-dimensional(2D) DOA estimation algorithm of coherent signals with a separated linear acoustic vector-sensor(AVS) array consisting of two sparse AVS arrays is proposed. Firstly,the partitioned spatial smoothing(PSS) technique is used to construct a block covariance matrix, so as to decorrelate the coherency of signals. Then a signal subspace can be obtained by singular value decomposition(SVD) of the covariance matrix. Using the signal subspace, two extended signal subspaces are constructed to compensate aperture loss caused by PSS.The elevation angles can be estimated by estimation of signal parameter via rotational invariance techniques(ESPRIT) algorithm. At last, the estimated elevation angles can be used to estimate automatically paired azimuth angles. Compared with some other ESPRIT algorithms, the proposed algorithm shows higher estimation accuracy, which can be proved through the simulation results.

Temporal-spatial subspaces modern combination method for 2D-DOA estimation in MIMO radar

A 2D-direction of arrival estimation (DOAE) for multi input and multi-output (MIMO) radar using improved multiple temporal-spatial subspaces in estimating signal parameters via rotational invariance techniques method (TS-ESPRIT) is introduced. In order to realize the improved TS-ESPRIT, the proposed algorithm divides the planar array into multiple uniform sub-planar arrays with common reference point to get a unified phase shifts measurement point for all sub-arrays. The TS-ESPRIT is applied to each sub-array separately, and in the same time with the others to realize the parallelly temporal and spatial processing, so that it reduces the non-linearity effect of model and decreases the computational time. Then, the time difference of arrival (TDOA) technique is applied to combine the multiple sub-arrays in order to form the improved TS-ESPRIT. It is found that the proposed method achieves high accuracy at a low signal to noise ratio (SNR) with low computational complexity, leading to enhancement of the estimators performance.

Joint DOA and polarization estimation for unequal power sources based on reconstructed noise subspace

In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

Subspace decomposition-based correlation matrix multiplication

The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.

SUBSPACE-BASED NOISE VARIANCE AND SNR ESTIMATION FOR MIMO OFDM SYSTEMS

This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR. Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.

基于实数化的均匀圆阵矩阵重构方法

为了减小低快拍数和低信噪比下采样协方差矩阵误差,并降低其运算复杂度,提出了一种基于实数化的均匀圆阵采样协方差矩阵重构方法。针对均匀圆阵的特点,通过组建特殊的基向量,构成特殊的重构矩阵。通过将采样协方差矩阵实数化,进一步降低了重构矩阵的复杂度。考虑到多通道不一致性对重构矩阵的影响,引入0位校正算法,提高了重构方法的稳健性。最后应用重构后的协方差矩阵进行子空间类波达方向估计(direction of arrival,DOA)。实验仿真证明,该特殊重构矩阵在实数化下与原矩阵重构能力相同;当快拍数为100、信噪比为0 dB时,双信源分辨力较重构前由74%提高到95%以上;理论重构运算复杂度降低到原来的53.99%。

基于随机数据驱动SDMD的电力系统区域惯量评估方法

准确评估系统惯量对于支持未来低惯量电力系统的系统安全运行至关重要。文中提出了一种随机数据驱动下基于机电响应特征的系统惯量评估方法,从随机响应信号中实现了区域有效惯量的评估。首先,通过将随机Koopman理论与随机动态系统相结合,推导出了随机Koopman空间上机电特征与系统惯量的耦合关系。然后,利用子空间动态模式分解(SDMD),以数据驱动的方式提取系统机电响应特征。该算法通过正交投影和奇异值分解,在压缩数据的同时实现了系统状态矩阵的低秩近似,可以有效减弱观测噪声对计算结果的影响。最后,采用IEEE 4机2区系统和IEEE 10机39节点系统的数值模拟算例验证了所提算法的有效性和鲁棒性。

基于矩阵重构和酉变换的DOA估计

为了提高重构相干信号测向算法的估计性能,降低算法运算量,提出了一种基于矩阵重构和酉变换方法的酉矩阵重构算法。该算法首先通过酉变换将阵列接收数据从复值计算转换为实值计算,使计算量大大降低;然后计算阵列协方差矩阵并进行特征值分解得到信号子空间,再将信号子空间重构为Toeplitz矩阵实现解相干并再次进行酉变换;最后通过特征值分解得到信号子空间并使用最小二乘法实现波达方向(direction of arrival,DOA)估计。相比于改进的旋转不变性的信号参数(estimation of signal parameters via rotational invariance techniques-like,ESPRIT-Like)算法和空间平滑处理算法,由于消除了噪声影响、构造了Toeplitz矩阵以及充分利用了数据的共轭信息,该算法的估计精度更高、具有更高的运算效率且在ESPRIT-Like算法失效的条件下新算法仍能有效估计DOA。本文算法的运行时间是ESPRIT-Like算法的71.2%,实验结果证明了该方法的有效性和真实性。

A BLIND SNR ESTIMATOR FOR DIGITAL BANDPASS SIGNALS

A subspace-based blind Signal-to-Noise Ratio (SNR) estimation algorithm for digital bandpass signals in Additive White Gaussian Noise (AWGN) channel is discussed. The lower bounds of the mean and variance of the estimation are derived, and simulations are performed for the commonly used digital bandpass signals, such as MPSK (M=2, 4, 8), MFSK (M=2, 4) and MQAM (M=16, 64, 128, 256) signals. Theoretical analyses and simulation results indicate that the proposed algorithm is ef- fective even when the SNR is below 0dB. Furthermore, the algorithm can provide a blind estimator in that it needs neither the parameters of the received signals, such as the carrier frequency, symbol rate and modulation scheme, nor the synchronization of the system.

基于分层人工鱼群的相干极化-DOA联合估计

智能优化算法是解决多维非线性优化问题、提高计算效率的有力工具。本文针对相干辐射源极化-空间角联合估计中计算量巨大的工程难题,以广义子空间拟合约束公式为代价函数,提出一种分层人工鱼群算法。该算法基于分层协同策略将鱼群分为底层和顶层,底层以人工鱼群算法进行全局搜索以保证种群多样性,顶层以粒子群算法进行局部搜索以加快收敛速度。仿真结果证明:分层人工鱼群算法能大幅降低广义子空间拟合的计算量,尤其是在较多目标的情况下。算法可有效提高计算效率,同时可提供优于传统人工鱼群算法的估计精度。

一种冲击噪声下相干分布源多峰DOA估计方法

针对现有相干分布源波达方向(Direction Of Arrival,DOA)估计方法计算量大、抗冲击噪声能力弱和不能有效去相干等难题,本文提出了一种冲击噪声下相干分布源多峰DOA估计方法,并推导了冲击噪声下相干分布源DOA估计的克拉美罗界.为了实现冲击噪声下相干分布源DOA估计,采用加权范数协方差抑制冲击噪声,进而首次推导出多峰加权信号子空间拟合方程,并设计了一种多峰量子秃鹰算法快速无量化误差求解.仿真结果表明,所提方法在冲击噪声下能够以较小的快拍数实现相干分布源DOA估计,且无需额外的解相干操作即可有效去相干.与一些已有的高精度DOA估计方法相比,所提方法仿真时间明显缩短,且具有更高的估计精度和估计成功概率,突破了已有相干分布源DOA估计方法的应用局限,可推广应用于其他复杂的DOA估计问题中.

复合高斯杂波下距离扩展目标斜对称自适应子空间检测器

针对非高斯杂波背景中距离维扩展的多秩线性子空间信号自适应检测问题,本文基于两步法设计了广义似然比(GLRT)、Rao、Wald检测器。为解决训练样本有限情况下自适应检测器性能急剧下降的问题,本文在检测器设计阶段利用了协方差矩阵的斜对称结构特征。考虑到高分辨率雷达目标回波除了具有沿雷达径向的平动分量之外,还能观测到偏航、横滚、俯仰等转动分量,将目标信号建模为距离扩展子空间信号,以充分利用多普勒信息;为弱化纹理分量先验分布选择的困难,减小模型失配导致的检测性能损失,将杂波建模为纹理分量为未知常数且散斑协方差矩阵具有斜对称结构的复合高斯杂波模型。经过蒙特卡洛仿真实验验证,在训练样本数量受限的情况下,本文所提三种斜对称检测器检测概率达到0.5所需的信杂比相比传统检测器平均低20 dB。同时,分析表明提出的三个斜对称自适应检测器均对散斑协方差矩阵具有恒虚警特性。

子空间信号失配下的加权自适应检测器

对于多通道雷达系统,通常存在阵元误差及旁瓣干扰,此时雷达系统假定的目标导向矢量与真实值不同,从而导致信号失配。针对子空间模型下的失配信号检测问题,提出了一种具有恒虚警特性的加权自适应检测器,得到了检测器解析的检测概率和虚警概率计算表达式。根据系统需要,通过调整加权系数,所提出的检测器能够实现对失配信号的灵活检测。此外,对无信号失配的检测环境,在合适的权系数下,所提出的检测器能够提供比现有检测器更高的检测概率。

基于子空间相关运算的GNSS欺骗干扰检测研究

针对全球导航卫星系统GNSS(global navigation satellite system)面临的欺骗干扰问题,提出了一种基于子空间相关运算的GNSS导航信号欺骗干扰检测算法,该方法以单周期伪随机序列的中频采样信号作为样本,依据设定的多普勒频移搜索步长,获得多组采样序列,并计算自相关矩阵,经特征值分解构建了以伪随机序列为基础的信号子空间。通过已知的伪随机序列循环移位对信号子空间进行相关运算,根据相关峰值数量判断是否存在欺骗干扰。仿真结果验证了算法的有效性,并从搜索范围、搜索步长、信噪比等多个角度验证了算法的鲁棒性,结果表明:本方法能够较好地抑制信号的普勒频移的影响,并在较低信噪比情况下仍具有较高的检测精度。

基于平方根分解的干扰加噪声背景下距离扩展目标子空间检测器

研究了子空间干扰加未知噪声背景下的距离扩展目标检测问题.将目标和干扰均建模为已知子空间内具有未知坐标的信号,且目标信号子空间包含在干扰信号子空间内.利用一组包含干扰和噪声的辅助数据估计干扰协方差矩阵,采用平方根分解方法变换干扰矩阵结构,进而求解干扰坐标协方差矩阵、噪声功率和目标信号坐标的最大似然估计,基于一步法设计策略建立广义似然比检验检测器.理论分析表明,所提出的检测器对干扰加噪声的协方差矩阵具有恒虚警率特性.仿真分析表明,在与目标信号相关的干扰加未知噪声背景下,所提出的距离扩展目标子空间检测器的检测性能优于已有对比检测器,且能有效抑制不同功率的子空间干扰信号.

基于常规扰动数据的电网分区及惯量在线辨识方法

鉴于电力系统转动惯量在大规模新能源接入后可能随运行场景变化而波动的问题,为满足电网安全稳定运行的要求,定期监测电力系统的等效惯量是必要的。因此提出了一套基于电网常规随机扰动数据的区域惯量辨识方法,首先提出采用总体最小二乘-旋转不变技术的信号参数估计算法对电网不同母线频率振荡成分进行提取,并根据提取结果对电网进行分区,以提升区域边界联络线功率波动监测数据的信噪比;然后提出利用子空间辨识算法对区域惯性中心频率与功率不平衡量之间的动态关系进行辨识,进一步计算区域惯量等参数。最后以IEEE 39节点系统为例,验证了所提基于分区结果进行惯量辨识方法的有效性。

Spectral-spatial Classification of Hyperspectral Images Using Signal Subspace Identification and Edge-preserving Filter

Hyperspectral images in remote sensing include hundreds of spectral bands that provide valuable information for accurately identify objects.In this paper,a new method of classifying hyperspectral images using spectral spatial information has been presented.Here,using the hyperspectral signal subspace identification(HYSIME)method which estimates the signal and noise correlation matrix and selects a subset of eigenvalues for the best representation of the signal subspace in order to minimize the mean square error,subsets from the main sample space have been extracted.After subspace extraction with the help of the HYSIME method,the edge-preserving filtering(EPF),and classification of the hyperspectral subspace using a support vector machine(SVM),results were then merged into the decision-making level using majority rule to create the spectral-spatial classifier.The simulation results showed that the spectral-spatial classifier presented leads to significant improvement in the accuracy and validity of the classification of Indiana,Pavia and Salinas hyperspectral images,such that it can classify these images with 98.79%,98.88% and 97.31% accuracy,respectively.

基于聚焦信号子空间估计导向矢量的干扰声源抑制方法

针对最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)波束形成器对导向矢量失配较敏感的问题,本文提出了一种有效的干扰声源抑制方法 .该方法首先将语音信号的频带划分为多个子带,通过聚焦信号子空间方法估计各子带的声源到达方向(Direction of Arrival,DOA),并采用统计直方图估计各声源的初始DOA;其次,为了减小导向矢量失配,利用声源的空间稀疏性,通过Capon功率构建目标声源导向矢量估计的代价函数,约束目标声源导向矢量远离干扰声源空间;最后,根据估计的导向矢量,估计干扰声源加噪声协方差矩阵,以获得MVDR波束形成器的权重.基于TIMIT语料库的实验结果证明,提出的干扰声源抑制方法的输出信干噪比(SINR)及语音质量感知评价(PESQ)优于参考方法,具有更佳的抗导向矢量失配性能.

基于斜投影算子的混合信号DOA估计算法

为了提高混合信号的波达方向(direction of arrival,DOA)估计精度并降低其阵列孔径损失,提出一种基于斜投影算子的高精度DOA估计算法.所提算法将混合信号中独立信号与相干信号分两个阶段进行估计,首先利用ESPRIT(estimating signal parameter via rotational invariance techniques)算法处理阵元接收数据的协方差矩阵,得到混合信号中独立信号的DOA估计值;而后利用斜投影算子去除混合信号中独立信号的信息,得到新的协方差矩阵;利用新得到的协方差矩阵的信号子空间进行去相干处理;最后结合ESPRIT算法计算得到相干信号的DOA估计值.仿真结果表明,相较传统的混合信号DOA估计算法,所提算法在低信噪比情况下以及信号入射间隔较小的情况下有较高精度,有效地降低了阵列孔径的损失.在不同的采样快拍数下,本文算法也表现出更强的鲁棒性.

基于子空间的宽带信号时延差与相位差估计方法

针对现有的Simple、Sumple等互相关类算法无法分辨多个信源的问题,结合子空间算法中的超分辨估计——Music算法,提出了基于子空间的子带相位差估计算法——Music-Simple。对Music-Simple算法的合成性能进行了仿真分析并与传统的Music算法进行对比,验证了Music-Simple算法的正确性。结果表明,Music-Simple具有分辨多个信源的能力,可以同时估计出多个信源在多个接收天线处的相位差,显著提升了对包含多个信源信号的合成性能。