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.

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.

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。

基于分子群改进海象优化算法的矢量水听器宽带信号DOA估计

针对矢量水听器阵列宽带信号波达方向(DOA)估计中存在误差大,求解精度不高等问题,提出一种将分子群改进的海象优化算法(MGIWaOA)与极大似然(ML)估计算法相结合的宽带信号估计方法。首先利用信号子空间变换算法构建聚焦矩阵,将宽带信号转换为窄带信号,然后建立基于ML的DOA估计模型,再通过MGIWaOA进行优化估计。仿真结果表明,该算法比海象优化算法、正弦余弦算法、麻雀优化算法、差分进化算法、遗传算法、粒子群算法的ML估计方法具有更快的收敛速度、更低的均方根误差和更稳健的性能。

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%。

基于自适应高斯勒让德积分的稳健波束形成算法

针对协方差矩阵重构过程中计算量大的问题,提出一种基于自适应高斯勒让德积分重构协方差矩阵并且改进导向矢量估计的稳健波束形成算法。首先构造干扰子空间,引入自适应高斯勒让德积分计算干扰子空间得到干扰相关矩阵,然后对干扰相关矩阵进行特征分解求出投影矩阵,并且将接收信号投影到投影矩阵上,求出重构的干扰加噪声协方差矩阵,最后对导向矢量失配进行修正。仿真结果表明,该算法设计出的波束形成器具有较好的应对导向矢量失配、阵元扰动的能力。

自适应指纹子空间匹配WiFi定位算法

传统的无线保真(WiFi)指纹匹配算法中,由于信号波动而导致的偏远邻近点与环境中物体对接入点(AP)信号遮挡等因素都会严重影响定位精度。针对这一问题,本文提出了一种自适应指纹子空间匹配定位算法。根据不同AP的组合将指纹库和测试指纹划分子空间,在每个子空间中利用欧氏距离之间的差值设置性能最优的临界值,筛选出最邻近的K个参考点;采用加权K近邻法进行粗定位,排除来自偏远邻近点带来的误差;最后整合各个子空间粗位置的估计值,采用平均滤波进行精确定位。实验结果表明,与传统的WiFi指纹匹配算法相比,所提算法有效减少了偏远邻近点和AP遮挡对定位精度的影响,增强了AP对不同位置的约束性,提高了WiFi定位系统的精度和鲁棒性。

面向同频干扰环境的5G机会信号定位算法研究

针对全球导航卫星系统(GNSS)拒止环境下定位精度难以保证的问题,该文设计了一种基于新无线电(NR)机会信号的定位方案,并提出一种基于干扰消除子空间追踪(ICSP)算法,解决超密集网络(UDNs)和异构网络(HetNets)环境中同频干扰对定位观测量提取精度不足的问题。通过仿真实验和通用软件无线电外设(USRP)半实物仿真,验证了ICSP算法在复杂网络环境中优化5G机会信号接收机性能、提高定位精度上的有效性。

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。

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.

多路径支撑集回溯贪婪重构算法

针对现有压缩感知贪婪算法容易陷于局部最优、过拟合等问题,提出一种稀疏恢复算法,称为多路径支撑集回溯贪婪重构(multipath backtracking greedy pursuit,MBGP)算法。该算法以最小残差为重构目标,对候选原子展开多条路径同时搜索,且每次筛选多个原子,通过回溯过程剔除误选的原子。基于有限等距性质给出MBGP算法重构信号的充分条件,以确保其从测量值精确恢复任何K-稀疏信号,并通过信号重构能力来评估MBGP算法的性能。数值实验结果表明,该算法在相同信号条件下,能够在采样数更少、稀疏度更大的场合下精确重构信号,且性能更逼近理想Oracle-最小二乘估计器。

基于宽带相干信号子空间方法的超声导波长骨评价

超声导波对长骨的生物力学特性敏感,已经广泛应用于长骨的健康评价。由于骨骼的高衰减特性,准确估计长骨中宽带相干导波信号的频散模式,进而准确反演长骨材料特征参数,是骨超声领域的一个研究热点和难点。本文提出了一种基于宽带相干信号子空间的导波波数估计方法,高精度提取导波频散模式;在此基础上,应用具有全局优化特性的蚁群算法从导波模式中估计长骨材料参数,成功实现了对长骨状况的定量评价。仿真和离体实验(2块仿真样本和2块离体牛骨)共同验证了方法的有效性,实验频散与参考频散之间的相对误差分别为3.52%,3.83%,3.35%,4.51%;对于离体牛骨,皮质骨厚度、纵波速度、横波速度估计值与参考值之间的平均相对误差分别为3.10%,0.11%,0.03%。综上,本文提出的方法为定量超声长骨评价提供了新的借鉴,也可应用于其他固体波导频散的高精度提取和结构健康表征。

基于子空间模型的稀疏贝叶斯DOA估计

为了提高相干信源条件下的离格波达方向(direction of arrival,DOA)估计精度,提出一种基于子空间模型的稀疏贝叶斯学习(sparse Bayesian learning,SBL)的DOA估计算法。该算法首先将子空间平滑(subspace smoothing,SS)技术与加权子空间拟合(weighted subspace fitting,WSF)技术结合,然后将此子空间模型应用于SBL算法,并将离散网格点视为动态参数,用期望最大化(expectation maximization,EM)算法迭代更新网格点位置。与传统稀疏恢复算法相比,该算法在估计误差及计算复杂度上均具有明显优势,并对信源数目的估计误差具有较强的鲁棒性。

直扩信号伪码周期及序列的估计算法

为了解决直扩信号伪码周期和序列估计的难题,提出了功率谱二次处理结合信号子空间分解的方法。该方法首先利用功率谱二次处理手段估计出直接序列扩频(DS-SS)信号的伪码周期,在此基础上采用信号子空间分解对DS-SS信号伪码序列进行盲估计。为了用信号子空间分解法对伪码序列实施精确估计,在其后加上了r1校正方法。计算机模拟结果表明,该方法在输入信噪比小于-14 dB时还能良好地工作。

超声相控阵与宽带阵列信号处理相结合的油中局部放电定位方法

提出了一种基于超声相控阵与宽带阵列信号处理的油中局部放电定位新方法。首先采用超声相控阵传感器直接检测局放超声宽带信号,并形成阵列模型;然后针对该超声波信号频段为50～300kHz的宽频特性,应用宽带阵列信号处理中的旋转信号子空间(rotational signal subspace,RSS)算法对其进行聚焦处理,通过聚焦矩阵将宽带阵列信号不同频率下的方向矩阵变换到同一中心频率,最大限度地保留原始信号的宽频空域信息,实现宽带信号方位信息的累积;最后应用多重信号分类法(multiple signal classification,MUSIC)与交叉定位原理对聚焦后窄带信号进行波达方向估计与空间定位。实验结果验证了对该定位方法的有效性。

子空间重构的一类自适应波束形成算法

针对多维阵列的全维自适应波束形成算法运算量大和小样本条件下性能下降问题,该文提出采用子空间重构技术的自适应波束形成算法(SRC-BF)。该方法利用多维域的全维阵列数据可分维构造的特点,首先在训练样本集上估计分维阵列数据的信号子空间;然后基于张量积性质重构全维信号子空间并自适应剔除交叉项;最后采用子空间投影算法计算自适应加权矢量。理论分析和仿真结果表明该方法具有较低的运算复杂度,能有效提高自适应波束形成算法在小样本条件下的输出信干噪比。

基于信号子空间转换与快速子空间测向算法的局部放电超声阵列信号测向方法

提出一种基于信号子空间转换法(signal subspacetransform,SST)与快速子空间测向算法(fast subspaceestimation of DOA,FDOA)的局放超声阵列信号高精度测向新方法。首先利用SST算法对局放超声阵列信号进行聚焦处理,使得原始信号的宽频空域信息被最大限度地保留,从而实现宽带信号方位信息的累积。然后采用FDOA对聚焦后的窄带信号进行波达方向估计,FDOA无需特征分解,无需估计整个协方差矩阵,可提高运算速度,且具有更高的测向精度。在此基础之上,应用医学宽频超声信号和4 4超声阵列传感器模型,进行局放超声阵列信号测向仿真研究,仿真结果表明,测向误差小于2,验证了该方法的正确性。