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-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.

A NOVEL APPROACH FOR DOA ESTIMATION IN UNKNOWN CORRELATED NOISE FIELDS

The key of the subspace-based Direction Of Arrival (DOA) estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.

Joint Noise Reduction and lp-Norm Minimization for Enhancing Time Delay Estimation in Colored Noise

Time delay estimation (TDE) is an important issue in signal processing. Conventional TDE algorithms are usually efficient under white noise environments. In this paper, a joint noise reduction and -norm minimization method is presented to enhance TDE in colored noise. An improved subspace method for colored noise reduction is first performed. Then the time delay is estimated by using an -norm minimization method. Because the clean speech signal form the noisy signal is well extracted by noise reduction and the -norm minimization method is robust, the TDE accuracy can be enhanced. Experiment results confirm that the proposed joint estimation method obtains more accurate TDE than several conventional algorithms in colored noise, especially in the case of low signal-to-noise ratio.

An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.

Orthogonal projection based subspace identification against colored noise

In this paper, a bias-eliminated subspace identification method is proposed for industrial applications subject to colored noise. Based on double orthogonal projections, an identification algorithm is developed to eliminate the influence of colored noise for consistent estimation of the extended observability matrix of the plant state-space model. A shift-invariant approach is then given to retrieve the system matrices from the estimated extended observability matrix. The persistent excitation condition for consistent estimation of the extended observability matrix is analyzed. Moreover, a numerical algorithm is given to compute the estimation error of the estimated extended observability matrix. Two illustrative examples are given to demonstrate the effectiveness and merit of the proposed method.

基于细节信息约束的遥感影像条带噪声去除模型

遥感影像在获取过程中会经常受到条带噪声的污染,降低遥感影像的视觉效果,对影像解译和反演等处理产生不利影响。当前一些主流的基于变分的条带噪声去除方法,虽然可以去除条带噪声,但是往往也会导致影像细节信息的严重丢失。基于上述问题,本文提出了一种基于细节信息约束的遥感影像条带噪声去除模型(DISUTV)。在DISUTV模型中,将所提出的基于双边滤波器与正交子空间投影的细节信息分离算子与单向全变分正则化项、群组稀疏正则化项及单向全变分正则约束项进行了有效结合,并采用交替方向乘子法对其进行求解,用于从条带噪声影像中获取不含有细节信息的高精度条带噪声。利用模拟数据与真实数据对本文方法的条带噪声去除能力、细节信息保持能力及稳健性进行了验证并与现有前沿方法进行了比较。试验结果表明,本文方法在去除条带噪声的同时能更好地保留影像的细节信息,并且呈现出了较好的定性与定量结果。

城轨列车全自动运行系统噪声子空间鲁棒预测控制方法

为建立与实际运行状态更吻合、预测精确度更高的城轨列车全自动运行(fully automatic operation, FAO)系统预测控制模型,根据城轨列车运行的历史数据,综合考虑城轨列车运行过程中的噪声干扰,采用子空间辨识方法得到含噪声的预测控制模型,控制过程不断加入实时采集的数据,迭代模型辨识的历史数据,在线更新模型参数,得到抗干扰能力较强的城轨列车FAO系统噪声子空间鲁棒预测控制器。分别采用幅值为0、2、5、10 km/h的随机干扰噪声,在软件MATLAB中进行仿真试验,对比分析城轨列车FAO系统噪声子空间鲁棒预测器与传统子空间预测控制器的预测精度。结果表明:在随机噪声干扰下,城轨列车FAO系统噪声子空间鲁棒预测控制器的预测精度较高,噪声幅值为10 km/h时预测精度比传统子空间预测控制器提高14.21%。城轨列车FAO系统噪声子空间鲁棒预测控制器可在强干扰运行状态下高精度跟踪给定运行曲线。

强干扰与通道误差条件下弱信号DOA估计的实验研究

在实际测向系统中,当弱信号和强干扰空间临近时,空间谱测向系统仅能对强干扰进行波达方向(Direction of Arrival,DOA)估计,弱信号DOA估计性能下降甚至失效。针对这一问题,研究了空间谱扩展噪声子空间算法结合通道幅相误差校正,在强干扰抑制条件下对弱信号进行DOA估计的方法。该方法对采样信号的噪声协方差进行去加权处理,并对空间谱扩展噪声子空间算法的空间谱导向矢量进行修正。基于通用软件无线电外设(Universal SoftwareRadioPeripheral,USRP)和印刷偶极子线形天线阵构建实验平台,实验结果证明空间谱扩展噪声子空间算法结合改进的通道幅相误差校正方法,能对临近干扰源进行空间谱抑制的同时,实现对弱信号的DOA估计。

一种奇异值分解与子空间加权联合的改进MUSIC算法

在低信噪比、小快拍数等非理想条件下,经典DOA估计算法对邻近目标的分辨率严重下降,甚至失去分辨能力。针对这一问题,提出了一种将重构的接收信号协方差矩阵进行奇异值分解并与改进的加权子空间方法相结合的改进算法。该算法充分利用互相关信息构建新的接收信号协方差矩阵,并对噪声子空间信息采用新的校正方法,对噪声特征值进行改造,之后对噪声子空间进行加权,最后与信号子空间加权技术相联合。仿真实验证明,改进算法在低信噪比和小快拍数条件下可以分辨间隔4°的相邻目标,统计分析表明该算法的分辨率明显优于经典MUSIC算法。

基于主成分分析的人体微多普勒图像质量评价研究

在老龄化社会背景下,借助雷达技术有效进行老年活动监控的关键,在于确保雷达微多普勒信息传递的精确性。所以提高人体运动微多普勒图像质量评价的准确性和鲁棒性至关重要。文章首先添加了不同级别的相位噪声图像及相应的主观评分数据,来扩充人体运动微多普勒图像质量评价(Human Motion Micro-Doppler Image Quality Assessment,HMMDIQA)数据库,增加数据库的多样性。并进一步提出了一套基于主成分分析子空间特征增强的算法进行人体运动微多普勒图像质量评估。在HMMDIQA数据库上的实验结果表明,相较于基础网络,所设计算法的各项评价指标都有所提升。

基于逻辑斯蒂回归的智能机器人语音指令识别方法

为了提高智能机器人语音指令识别精度,本文提出了基于逻辑斯蒂回归的智能机器人语音指令识别方法。采用多维语音传感器,对语音指令信号进行了采集,利用子空间降噪方法对语音指令信号进行了降噪处理。通过频谱特征参数估计方法进行了特征参数估计,构建了智能机器人语音指令的协方差融合模型,融合语音指令参数。采用逻辑斯蒂回归分析方法,结合稀疏性估计,实现了智能机器人语音指令识别。仿真结果表明,本文所提出方法的智能机器人语音指令识别精度较高,能够有效提升智能机器人的控制能力。

基于特征子空间的数字水印技术

根据特征子空间的原理 ,提出了一种新颖的基于特征子空间的数字水印算法 .水印可以嵌入到不同的子空间中从而可以发展为多水印算法 .由于特征子空间之间的正交特性 ,不同子空间中的水印相互独立 ,其稳健性不随水印数目的增加而变化 .水印强度参数根据视觉系统的照度掩蔽和纹理掩蔽特性适当地进行调整 ,从而同时保证二值水印图像的稳健性和不可见性 .该文就单水印 ,双水印以及多水印情形进行了仿真并进行JPEG压缩、图像缩放、剪切等失真测试 .

基于噪声子空间解析形式的快速DOA估计算法

该文针对特殊的信号环境各辐射源信号均值相等且不为零,利用均匀线阵导向矢量的Vandermonde结构,推导出了噪声子空间的解析形式,并以此为基础提出了利用均匀线阵和稀疏平面阵的1维和2维DOA估计快速算法。该算法不需要计算接收数据的协方差矩阵,也不需要任何矩阵分解,因此计算量远小于传统的超分辨DOA估计,而且无论信号之间是否具有相干性,该方法有相同的估计性能。仿真实验表明,在噪声均值为零且快拍数足够的条件下,该方法的估计性能整体上与Root-MUSIC算法相当,而在信噪比较低时性能优于后者。

基于随机子空间结合稳定图的间谐波高精度检测方法

为精确检测间谐波参数,提出了基于随机子空间辨识(SSI)与稳定图法相结合的间谐波检测方法。SSI算法可以直接根据含有系统特征信息的端部量测数据检测出系统的参数,首先结合稳定图检测出间谐波分量的个数和模型阶次,然后用SSI算法直接对原始含噪信号精确检测出各间谐波分量的实际频率,如果信号的信噪比大于10dB,则无须去噪,用最小二乘法对各间谐波分量的幅值进行精确检测。分别针对间谐波数值仿真信号、含噪调幅信号及电弧炉电流信号进行间谐波分析,结果表明:该方法抗噪声能力强、计算速度快、检测精度高,尤其对时变性较强的调幅性间谐波信号具有强适应性。仿真结果验证了该方法的可行性与有效性。

改进的匹配追踪在方波信号滤波中的应用

为了克服常规滤波方法对方波信号滤波能力的不足,实现对方波信号的精确滤波,提出了一种改进的匹配追踪算法。针对方波信号特征,构建了与方波信号匹配而对噪声不敏感的方波原子;基于正交匹配追踪,并吸收子空间追踪的回溯思想,改进了最优原子选择方法;鉴于有用信号与噪声信号的能量差异,使用了一种自适应迭代停止标准,能准确找到有用信号和噪声的临界点,解决噪声能量未知的预估问题。对不同信噪比下的仿真方波信号进行滤波,经实测验证,所提方法在信噪比和均方误差方面都优于常规去噪算法,且保留了方波的特征,适用于方波信号的滤波。

强信号背景下基于噪声子空间扩充的弱信号DOA估计方法

针对强信号背景下弱信号波达方向(direction of arriaval,DOA)估计问题,提出了一种基于噪声子空间扩展的弱信号DOA估计算法。该算法提出并使用了噪声子空间扩充的思想,其先将强信号导向矢量所在空间纳入噪声子空间进而构造出扩展噪声子空间,再在该扩展噪声子空间基础上利用常规多信号分类(multiple signalclassification,MUSIC)算法得到弱信号的DOA估计。通过噪声子空间的扩充有效地抑制了强信号谱峰,算法无需已知强信号方向及导向矢量,运算量与常规MUSIC相当。理论分析表明该算法对弱信号DOA估计性能不劣于对应的强信号阻塞类算法,仿真实验证实了其有效性和可行性。

零陷加深的自适应波束形成技术研究

在干扰方向和功率稳定的情况下,传统的自适应数字波束形成器能有效抑制干扰、提高信干噪比,但在干扰功率起伏的环境下,自适应数字波束形成器不能持续可靠地抑制干扰。针对这一问题,给出了一种增强信号中的干扰分量,再进行自适应波束形成的处理方法。该方法能根据需要对自适应数字波束形成器的自适应零点的零深进行调整,提高系统的抗干扰性能。理论分析和仿真实验验证了该方法的可行性。

子空间与维纳滤波相结合的语音增强方法

针对复杂背景噪声下语音增强后带有音乐噪声的问题,提出一种子空间与维纳滤波相结合的语音增强方法。对带噪语音进行KL变换,估计出纯净语音的特征值,再利用子空间域中的信噪比计算公式构成一个维纳滤波器,使该特征值通过这个滤波器,从而得到新的纯净语音特征值,由KL逆变换还原出纯净语音。仿真结果表明,在白噪声和火车噪声的背景下,信噪比都比传统子空间方法有明显提高,并有效抑制了增强后产生的音乐噪声。

烟花算法优化的软子空间MR图像聚类算法

现有的软子空间聚类算法在分割MR图像时易受随机噪声的影响,而且算法因依赖于初始聚类中心的选择而容易陷入局部最优,导致分割效果不理想.针对这一问题,提出一种基于烟花算法的软子空间MR图像聚类算法.算法首先设计一个结合界约束与噪声聚类的目标函数,弥补现有算法对噪声数据敏感的缺陷,并提出一种隶属度计算方法,快速、准确地寻找簇类所在子空间;然后,在聚类过程中引入自适应烟花算法,有效地平衡局部与全局搜索,弥补现有算法容易陷入局部最优的不足.EWKM,FWKM,FSC,LAC算法在UCI数据集、人工合成图像、Berkeley图像数据集以及临床乳腺MR图像、脑部MR图像上的聚类结果表明,所提出的算法不仅在UCI数据集上能够取得较好的结果,而且对图像聚类也具有较好的抗噪性能,尤其是对MR图像的聚类具有较高的精度和鲁棒性,能够较为有效地实现MR图像的分割.