信号DOA和极化信息联合估计的降维四元数MUSIC方法

基于简化电磁矢量传感器阵列,该文提出了一种新的降维四元数MUSIC估计方法。文中引用了四元数的概念,利用四元数的正交特性能够很好地描述矢量传感器阵元的正交结构这一优点,建立了电磁矢量传感器阵列的四元数模型,利用降维Q-MUSIC (Quaternion-MUSIC)方法先对极化信号DOA进行估计,通过已经估计出来的DOA信息,再借助传统的V-MUSIC (long-MUSIC)方法估计极化信息。从而依次获得极化信号的4个参数。仿真实验验证了算法的可行性。     

极化信号频率、二维到达角和极化参数的同时估计

本文研究基于标量传感器和/或空域完全伸展矢量传感器极化敏感时间双采样、空间欠采样阵列的多个宽频段窄带极化信号中心频率、二维波达方向和极化参数的同时估计问题。针对时、空多分辨采样数据,利用旋转不变参数估计方法分别估计出与信号频率有关的时间相位因子和对应的含有空间相位信息的所谓混合导向矢量。对估计出的信号时间相位因子的模和相角进行联合,获得模拟频率估计:利用混合导向矢量中还包含的与空间采样间隔无关的极化-角度相干结构信息去除方向余弦估计中的整周模糊并获得相应的极化参数估计。     

基于累积量的极化敏感阵列信号DOA和 极化参数的同时估计

本文研究极化敏感阵列非高斯窄带信号二维波达方向(DOA)和极化参数的同时估计问题.所考虑的极化敏感阵列包含一个特定子阵,该子阵由空间稀疏伸展三极子天线和两个导引阵元组成.分析了3维极化-角度域相干结构的平凡模糊问题,对利用并行累积量ESPRIT分别恢复出的信号3维极化-角度域相干结构和空域相干结构信息进行联合完成了非线性极化信号二维DOA和极化参数的同时估计.给出了一种相位缠绕解决方案并讨论了空间伸展三极子天线的可选择结构.     

基于双电磁矢量传感器的近场源多参数估计

基于双电磁矢量传感器(EVSs),提出一种新的近场源频率、波达方向(DOA)、距离和极化参数联合估计方法.该方法首先计算近场源的频率以及其在两个电磁矢量传感器处的二维DOA和极化参数,然后利用几何方法或搜索方法,得到近场源的距离估计.该方法仅需要两个电磁矢量传感器,节约了阵列孔径,适合于阵列孔径有限的场合.估计过程中仅需一次互相关以及特征分解等简单的运算,且各参数自动配对,节省了运算量.实验结果验证了该方法的有效性.     

矢量传感器阵列MIMO雷达高精度二维DOA与极化联合估计

该文采用矢量传感器配置下的十字型阵列MIMO雷达系统,提出一种新的2维高精度DOA与极化参数联合估计算法。首先根据MIMO雷达虚拟阵列导向矢量的特点,通过降维矩阵的设计及回波数据的降维变换,将高维回波数据转换至低维信号空间;然后基于传播算子获得对应信号子空间的估计,利用收、发阵列阵元间长基线对应的旋转不变性和极化矢量中电场矢量和磁场矢量的叉积进行2维高精度DOA估计和解模糊处理,同时利用与阵列结构无关的极化域旋转不变性进行极化辅角和极化相位差的联合估计。该矢量传感器MIMO雷达阵列可同时获取MIMO雷达的波形分集和矢量传感器的极化分集,无需额外增加阵元和硬件开销,能够有效扩展阵列孔径,提高参数估计性能;同时通过降维变换及传播算子,在获取信噪比增益的同时,能够实现2维高精度DOA和2维极化矢量的联合估计及参数的自动配对,有效降低数据处理维数和参数估计的运算复杂度;最后,仿真结果验证了理论分析的正确性和算法的有效性。     

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。     

基于极化敏感阵列均匀线阵的二维DOA估计

针对残缺电磁矢量传感器的极化敏感阵列多参数联合估计问题,该文提出一种基于正交偶极子的均匀线阵的2维波达方向(Direction-Of-Arrival, DOA)估计算法。首先,对极化敏感阵列的接收数据矢量的协方差矩阵进行特征分解,然后将信号子空间划分成4个子阵,根据旋转不变子空间(ESPRIT)算法分别求出其中1个子阵与其它3个子阵的相位差,再对不同子阵间的相位差进行配对,最后根据相位差求出信号的DOA估计和极化参数。由正交偶极子组成的均匀线阵使用极化MUSIC算法和传统ESPRIT算法无法进行2维DOA估计,该文提出的算法解决了这个问题,并且相较于极化MUISC算法降低了算法的复杂度。仿真结果验证了该文算法的有效性。     

稀疏拉伸式L型极化敏感阵列的二维波达方向和极化参数联合估计

为降低现有的共心式矢量传感器阵列天线间存在的严重互耦影响,进一步提高参数估计精度,该文提出一种稀疏拉伸式L型极化敏感阵列(SSL-PSA),并针对该阵列提出一种2维波达方向(DOA)和极化参数联合估计算法。首先建立稀疏拉伸式极化敏感阵列的信号模型,然后将阵列划分为6个子阵,采用子空间旋转不变算法(ESPRIT)算法得到多个旋转不变因子(RIFs),再根据旋转不变因子间的关系,通过数学运算,得到一组方向余弦有模糊精估计值和4组无模糊粗估计值;然后重构出对应的4组导向矢量,根据导向矢量和噪声子空间的正交性,确定出正确的一组无模糊粗估计值;最后通过现有的解模糊方法得到高精度且无模糊的DOA和极化参数估计值。该文所提阵列不存在共心结构,相对于现有的含有共心式矢量传感器结构的阵列,大大降低了互耦影响,且可在不增加天线数目的前提下,有效扩展阵列的2维孔径,大大提高DOA估计精度。仿真结果证明该文所提方法的有效性。     

单有向电磁矢量传感器的参数估计方法

传统的单电磁矢量传感器(UEMVS)由3个电偶极子和3个磁环构成且方向图是全向的。但是当多个单电磁矢量传感器依附在共形载体上构成共形电磁矢量传感器阵列时,为了降低共形电磁矢量传感器阵列的副瓣,通常每个传感器的方向图是有向的。基于有向方向图的单电磁矢量传感器也称为单有向电磁矢量传感器(UDEMVS)。该文针对UDEMVS的参数估计问题,提出两种参数估计方法,分别是基于免搜索的旋转不变信号参数估计和矢量叉积(ESPRIT-VCP)方法以及基于网格搜索的多重信号分类和最小瑞利商(MUSIC-MRQ)方法。ESPRIT-VCP方法是根据旋转不变性和矢量叉积,获得4维参数的闭式解,MUSIC-MRQ方法根据信号和噪声子空间正交性与最小瑞利商,利用网格搜索得到2维角度估计值,进而结合信号回波模型得到2维极化的估计值。所提两种方法只利用了UDEMVS的6通道数据就能有效得到目标的参数估计结果,有较低的计算复杂度。仿真结果从角度和极化的估计性能出发验证了所提方法的有效性。     

基于矢量传感器MIMO雷达的DOD DOA和极化联合估计算法

该文研究了一种基于多输入多输出(MIMO)电磁矢量传感器阵列雷达目标波离角(DOD),波达角(DOA)和极化联合估计问题。提出一种新型矢量阵MIMO雷达系统模型,发射阵列采用常规阵元,而接收阵列采用电磁矢量传感器。在此基础上,该文提出4维MUSIC, ESPRIT和迭代1维MUSIC 3种联合参数估计算法。其中迭代1维MUSIC算法首先利用矢量传感器的内在结构特点获得目标DOA预估计,随后采用MUSIC算法对DOD和DOA分别进行1维搜索获得目标角度的高精度估计,最后给出一种基于ESPRIT的目标极化估计算法。迭代1维MUSIC算法可用于不规则阵列,对接收阵列约束较少,无需2维搜索及多维搜索,还可以利用矢量阵特点扩展阵列孔径提高DOA估计精度。此外,论文还推导了DOD, DOA和极化联合估计的CRB。仿真实验表明,与前两种算法相比,迭代1维MUSIC算法具有与CRB更接近的估计精度。     

基于微型光机电系统扫描镜技术的激光散斑抑制方法

激光固有的时间和空间相干性,造成的散斑现象成为实现激光显示技术的瓶颈。针对激光显示的消散斑问题,提出了一种基于微型光机电系统(MOEMS)扫描镜技术的散斑抑制方法,并设计了一种简单的二维扫描镜结构,为开发非运动式、小体积、低功耗、高速低成本电调制的MOEMS,实现对激光散斑的抑制,标准化消相干器件的工艺的开发和规模化生产技术提供了参考。     

Probabilistic and sequential computation of optical flow usingtemporal coherence

In the computation of dense optical flow fields, spatial coherence constraints are commonly used to regularize otherwise ill-posed problem formulations, providing spatial integration of data. We present a temporal, multiframe extension of the dense optical flow estimation formulation proposed by Horn and Schunck (1981) in which we use a temporal coherence constraint to yield the optimal fusing of data from multiple frames of measurements. Conceptually, standard Kalman filtering algorithms are applicable to the resulting multiframe optical flow estimation problem, providing a solution that is sequential and recursive in time. Experiments are presented to demonstrate that the resulting multiframe estimates are more robust to noise than those provided by the original, single-frame formulation. In addition, we demonstrate cases where the aperture problem of motion vision cannot be resolved satisfactorily without the temporal integration of data enabled by the proposed formulation. Practically, the large matrix dimensions involved in the problem prohibit exact implementation of the optimal Kalman filter. To overcome this limitation, we present a computationally efficient, yet near-optimal approximation of the exact filtering algorithm. This approximation has a precise interpretation as the sequential estimation of a reduced-order spatial model for the optical flow estimation error process at each time step and arises from an estimation-theoretic treatment of the filtering problem. Experiments also demonstrate the efficacy of this near-optimal filter.     

Measurement matrix optimization based on incoherent unit norm tight frame

This paper considers the problem of measurement matrix optimization for compressed sensing (CS) in which the dictionary is assumed to be given, such that it leads to an effective sensing matrix. Due to important properties of equiangular tight frames (ETFs) to achieve Welch bound equality, the measurement matrix optimization based on ETF has received considerable attention and many algorithms have been proposed for this aim. These methods produce sensing matrix with low mutual coherence based on initializing the measurement matrix with random Gaussian ensembles. This paper, use incoherent unit norm tight frame (UNTF) as an important frame with the aim of low mutual coherence and proposes a new method to construction a measurement matrix of any dimension while measurement matrix initialized by partial Fourier matrix. Simulation results show that the obtained measurement matrix effectively reduces the mutual coherence of sensing matrix and has a fast convergence to Welch bound compared with other methods.     

Region-level motion-based background modeling and subtraction using MRFs.

This paper presents a new approach to automatic segmentation of foreground objects from an image sequence by integrating techniques of background subtraction and motion-based foreground segmentation. First, a region-based motion segmentation algorithm is proposed to obtain a set of motion-coherence regions and the correspondence among regions at different time instants. Next, we formulate the classification problem as a graph labeling over a region adjacency graph based on Markov random fields (MRFs) statistical framework. A background model representing the background scene is built and then is used to model a likelihood energy. Besides the background model, a temporal coherence is also maintained by modeling it as the prior energy. On the other hand, color distributions of two neighboring regions are taken into consideration to impose spatial coherence. Then, the a priori energy of MRFs takes both spatial and temporal coherence into account to maintain the continuity of our segmentation. Finally, a labeling is obtained by maximizing the a posteriori energy of the MRFs. Under such formulation, we integrate two different kinds of techniques in an elegant way to make the foreground detection more accurate. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.     

Speckle reduction in coherent information processing

Speckle reduction techniques have been developed as one of the most active research fields in coherent optical information processing, together with the investigations of speckle statistics and coherence theory. The principles of speckle reduction are classified to five categories: 1) control of spatial coherence, 2) control of temporal coherence, 3) spatial sampling, 4) spatial averaging, and 5) digital image processing. This paper surveys the research works in the field of speckle reduction techniques involving four categories 1)-4) which have been conducted in the past 30 years     

基于三分量电磁矢量传感器的波达角和极化参数估计

针对传统的降维四元数旋转不变子空间算法(Dimension Reduction Quaternion Estimation of Signal Parameters via Rotational Invariance Techniques, DRQ-ESPRIT)存在“四元数模型相干”和孔径损失问题, 改进了DRQ-ESPRIT算法, 并提出了伪虚拟对称扩展孔径四元数旋转不变子空间算法(Fake Virtual Symmetrical Aperture Expansion Quaternion Estimation of Signal Parameters via Rotational Invariance Techniques, FVSAEQ-ESPRIT).所提算法通过修正极化角度域导向矢量和阵元空间相移矢量的乘法顺序, 解决了“四元数模型相干”问题, 并利用导向矢量的虚拟对称操作和Khatri-Rao子空间方法, 增加了极化敏感阵列的自由度, 提高了波达角(Direction of Arrival, DOA)和极化参数的估计精度.最后, 仿真实验验证了所提算法的有效性.     

空间一致性邻域保留嵌入的高光谱数据特征提取

局部线性嵌入(LLE)和邻域保留嵌入(NPE)等流形学习方法可以提取高光谱数据的主要结构特征,有助于对数据的理解和进一步处理。但是,这些方法忽视了高光谱图像中相邻像素之间的相关性。针对这个问题,提出一种基于空间一致性思想的邻域保留嵌入(SC-NPE)特征提取算法,通过一个优化的局部线性嵌入,并考虑相邻像素的相关特性,在高维空间建立数据的局部邻域结构。然后寻找一个优化的变换矩阵,将局部邻域结构投影到低维空间,实现数据的特征提取。与LLE和NPE算法相比,SC-NPE既考虑高光谱数据的流形结构,又考虑了其图像域空间信息,可以更好地应用在高光谱数据的特征提取过程中。实验结果表明,SC-NPE特征提取算法在高光谱图像分类方面的性能明显优于其他同类算法。     

Hologram coherence effects

Holograms formed from opaque particles in a clear aperture illuminated with quasi-monochromatic, partially coherent light are considered. The method used to formulate the general boundary-value problem for the pair of wave equations propagating the mutual-coherence function is described. The effect of a reduction in spatial coherence on the resolution limit of a Fraunhofer (far-field) hologram is discussed. Bandwidth considerations of the same hologram define a usable far-field region from which resolvable reconstructions can be expected. The effects of spatial coherence upon Fresnel (near-field) holograms are shown, and experimental results that confirm these calculations are presented.     

Joint angle and Doppler frequency estimation of coherent targets in monostatic MIMO radar

This paper discusses the problem of joint direction of arrival (DOA) and Doppler frequency estimation of coherent targets in a monostatic multiple-input multiple-output radar. In the proposed algorithm, we perform a reduced dimension (RD) transformation on the received signal first and then use forward spatial smoothing (FSS) technique to decorrelate the coherence and obtain joint estimation of DOA and Doppler frequency by exploiting the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. The joint estimated parameters of the proposed RD-FSS-ESPRIT are automatically paired. Compared with the conventional FSS-ESPRIT algorithm, our RD-FSS-ESPRIT algorithm has much lower complexity and better estimation performance of both DOA and frequency. The variance of the estimation error and the Cramer–Rao Bound of the DOA and frequency estimation are derived. Simulation results show the effectiveness and improvement of our algorithm.     

基于贪婪算法的高分辨信号源DOA估计

确定辐射源的来波方向(DOA)是阵列信号处理的重要研究内容,已经广泛应用于雷达、声纳和无线通信等领域。本文研究了远场窄带信号源的DOA高分辨估计问题。利用信号来波方向在空域具有稀疏性的特点,建立了远场窄带信号源的稀疏表示模型。根据协方差矩阵的特征值分解和贪婪匹配追踪算法原理提出了一种基于特征值分解的多重正交匹配追踪算法（EIG MOMP）。首先,利用特征值分解对阵列接收数据进行降维处理。这一降维操作使得问题转化为了一个具有多重观测向量（MMV）的欠定方程求解问题。接着利用MOMP算法对降维后的数据进行处理,最终得到信号的DOA估计值。该算法实现了在低信噪比下远场窄带信号源的高分辨DOA估计,并具有较低的运算复杂度。将本文提出的算法与传统的Capon算法、多重信号分类算法（MUSIC）以及正交匹配追踪算法（OMP）进行了对比。结果证明,该算法在低信噪比下能取得较好的DOA估计效果,可以针对任意的相干信号源,并且具有高分辨率的优点。