一种用于未知噪声背景中高分辨DOA估计的方法

本文基于噪声在空间有限范围内相关的假设，采用两个具有足够空间间距的阵列，应用正则相关分析原理，给出了一种未知噪声背景中有效地估计信号子空间的方法，并利用与TLS－ESPRIT相类似的算法进行了DOA估计，计算机模拟实验表明：该方法具有较好的方向估计性能。     

基于连续相位稠密匹配的结构光三维重建优化方法

提出了一种基于连续相位稠密匹配的结构光三维（3D）重建优化方法。利用标定的相机阵列获取投影的结构光变形条纹,利用格雷码划分相位级次,提出等相位约束条件,实现不同相机图像之间的稠密匹配;然后利用空间3D点与相机阵列之间的投影关系,建立各相机位姿与重建点云全局优化模型;利用观测值反向投影的欧式距离建立非线性优化的目标函数,并将此优化问题转为李代数上的图优化模型进行迭代求解。利用公开数据集和实测数据进行实验,结果表明,所提方法可以实现360°无遮挡的3D重建,且重建精度相对传统无优化结构光方式提高了6.94%,相机位姿精度提高了9.77%。所提方法相比参考方法对相机畸变标定精度要求低,并可用于模型点云的非刚体融合。同时由于相机阵列的使用,重建过程中目标物体无需多次旋转,使得物体的3D重建更加方便。     

一种基于导向矢量变换的DOA估计预处理方法

针对大多数空间谱估计算法在目标数量大于阵列自由度时失效这一问题,该文基于内插阵列变换思想,提出一种基于导向矢量变换的空域滤波方法。利用目标分布的先验信息,将观测空间划分为若干子空间,保证每一子空间内的目标数量小于阵列自由度;使用内插阵列变换方法,将整个观测空间的导向矢量变换为子空间的导向矢量,抑制子空间外的目标,从而将观测空间内大量目标的DOA估计问题转化为若干子空间内的常规DOA估计问题。计算机仿真结果验证了方法的有效性。     

基于Leap Motion手势识别的悬浮真3D显示实时交互系统

提出基于Leap Motion手势识别的悬浮真3D显示实时交互系统,可实现对多个悬浮3D图像的独立交互.系统由显示和交互两个模块组成,显示模块利用集成成像3D显示屏再现3D图像,再通过二面角反射镜阵列实现悬浮真3D显示;交互模块通过Leap Motion识别手部各关节空间位置,提取手势信息并判断用户操作意图,利用提出的...     

基于阵列内插的双基地互质MIMO雷达角度联合估计方法北大核心CSCD

针对互质阵列形成的差分共阵(DCA)存在空洞导致虚拟阵列不连续的问题,提出了一种基于阵列内插形成无孔互质阵的双基地多输入多输出雷达测向方法。首先,推导阵列内插的位置与形成的DCA填补空洞的位置,并推导出自由度闭合式;其次,分别在发射阵列和接收阵列将子阵列内插,形成连续的虚拟发射阵列和虚拟接收阵列;然后,利用选择矩阵使等效虚拟信号向量在虚拟发射阵列和虚拟接收阵列中平滑移动得到空间平滑矩阵;最后,利用空间平滑矩阵进行空间谱估计,并通过多重信号分类算法实现角度自动匹配。     

集成成像立体显示技术性能改进研究

集成成像是一种利用微透镜阵列来记录和再现3D空间场景的真3D显示技术.本文主要针对传统集成成像技术中存在的黑边、视角窄、记录速度慢和深度计算不准确等问题,进行了深入地分析与研究.文中建立了相应的数学模型,分别提出了消黑边的计算机二次记录和大视角的集成成像3D显示以及快速计算机集成成像方法,依据建立的高斯光束分布模型,还...     

共形阵列幅相误差校正快速算法

基于子空间的联合迭代算法可以实现对空间信源方位和阵列幅相误差参数的联合估计。当对共形阵列进行幅相误差校正时,由于其空域导向矢量不具有Vander monde结构,导致快速高分辨空间谱估计方法无法直接应用,而利用2维谱峰搜索实现空间方位估计的运算量较大,限制了算法在共形阵列上的应用。针对此问题,该文提出一种借助虚拟阵列实现共形阵列幅相误差校正的新方法。该方法利用虚拟阵列的特殊结构快速实现对信源的DOA估计,省去了谱峰搜索过程,因而运算复杂度低,便于工程实现。理论分析和仿真结果验证了所提算法的有效性,可为共形阵列的工程应用提供参考。     

基于多空间光调制器拼接的空间悬浮全息真三维显示

全息技术是实现空间悬浮真三维（3D）显示的重要方法。空间光调制器（SLM）作为当前唯一的实时动态全息真3D图像投射仪器，像素量、分辨率等不足限制了其在空间悬浮真三维显示领域的应用。研究多SLM拼接实现高分辨低噪声的空间悬浮真3D显示，首先通过菲涅耳层析法结合空间坐标变换技术，计算获得3D物体360°视角的高分辨率全息图；然后将每张全息图分为相同分辨率的4幅图，加载到阵列式拼接的4个SLMs上，滤除一阶之外的光束后，再现出完整的具有高信息容量高分辨率的全息3D实像；最后利用超声雾化介质进行承载，实现实时动态空间悬浮真3D显示。另外，利用时间平均法对重建像进行噪声抑制研究，实验结果证明该方法可有效地提升空间悬浮全息显示图像的质量。     

电磁矢量传感器阵列相干信号源波达方向和极化参数的同时估计:空间平滑方法

研究如何利用电磁矢量传感器阵列中隐含的冗余空域信息解决多个相干极化信号源的二维波达方向(DOA)和极化参数的同时估计问题。基于整个阵列中所隐含的多个空域旋转不变结构,将组成阵列的单个或多个电磁矢量传感器单元看作一个无模糊子阵,利用空间平滑方法对阵列数据进行预处理,以恢复信号协方差矩阵的秩特性。在此基础上,利用多信号分类方法(MUSIC)和旋转不变参数估计方法(ESPRIT)完成多个相干极化信号源的二维 DOA 和极化参数的同时估计。文中还讨论了成功进行信号解相干的必要条件,并通过计算机仿真验证和比较了所给方法的有效性及其辨识能力。     

基于数据矩阵奇异值分解的时—空二维信号处理

本文提出一种利用阵列接收信号数据矩阵奇异值分解的时－空二维信号谱估计方法，该方法对线阵接收信号构成的数据矩阵进行奇异值分解，构造出噪声奇异向量，利用信号空间与噪声空间的正交性获得时－空二维谱，该方法可用于高分辨测向测速系统中，计算机模拟表明了该算法的有效性。     

Total least squares phased averaging and 3-D ESPRIT for jointazimuth-elevation-carrier estimation

The method of total least squares (TLS) phased averaging for high-performance subspace fitting in the three-dimensional (3-D) case of spectral estimation with 3-D ESPRIT is introduced and applied to the joint azimuth elevation-carrier estimation problem with two-dimensional (2-D) uniform rectangular arrays. The method is highly efficient computationally and is suitable for large arrays. Detailed computer experiments and comparisons are provided. For a 16×16 array of sensors and heavy noise, TLS phased-averaging 3-D ESPRIT exceeds the 3-D TLS unitary ESPRIT estimator by 300% in RMSE performance     

Two-dimensional minimum free energy spectral estimation

The author proposes a 2-D extension of the minimum free energy (MFE) parameter estimation method which may be used to determine autoregressive (AR) model parameters for 2-D spectral estimation. The performance of the technique for spectral estimation of 2-D sinusoids in white noise is demonstrated by numerical example. It is seen that MFE can provide superior spectral estimation over that which can be achieved with the multidimensional Levinson algorithm with equivalent computational burden. The performance of the technique in terms of computational expense and accuracy of spectral estimation over a number of simulation trials is compared with a modified covariance technique     

A Decoupled Approach for Near-Field Source Localization Using a Single Acoustic Vector Sensor

This paper considers the problem of three-dimensional (3-D, azimuth, elevation, and range) localization of a single source in the near-field using a single acoustic vector sensor (AVS). The existing multiple signal classification (MUSIC) or maximum likelihood estimation (MLE) methods, which require a 3-D search over the location parameter space, are computationally very expensive. A computationally simple method previously developed by Wu and Wong (IEEE Trans. Aerosp. Electron. Syst. 48(1):159–169, 2012), which we refer to as Eigen-value decomposition and Received Signal strength Indicator-based method (Eigen-RSSI), was able to estimate 3-D location parameters of a single source efficiently. However, it can only be applied to an extended AVS which consists of a pressure sensor separated from the velocity sensors by a certain distance. In this paper, we propose a uni-AVS MUSIC (U-MUSIC) approach for 3-D location parameter estimation based on a compact AVS structure. We decouple the 3-D localization problem into step-by-step estimation of azimuth, elevation, and range and derive closed-form solutions for these parameter estimates by which a complex 3-D search for the parameters can be avoided. We show that the proposed approach outperforms the existing Eigen-RSSI method when the sensor system is required to be mounted in a confined space.     

Efficient 2-D DOA and frequency estimation for L-shaped array via RD-PM

This paper addresses the issue of joint two-dimensional direction of arrival (2-D DOA) and frequency estimation via reduced-dimensional propagator method (RD-PM) with L-shaped array. The proposed algorithm has no need for eigenvalue decomposition of the sample covariance matrix and simplifies three-dimensional global spectral search within the three-dimensional propagator method (3-D PM) to one-dimensional local search, which greatly reduces computational complexity. Furthermore, the proposed algorithm can work under both uniform and non-uniform L-shaped array and can achieve paired 2-D DOA and frequency estimates automatically. In addition, the 2-D DOA and frequency estimation performance for the proposed method is approximate 3-D PM algorithm and parallel factor (PARAFAC) method but exceeds the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and improved PM algorithm. The detailed derivation of Cram´er-Rao bound (CRB) is provided and the simulation results demonstrate the effectiveness and superiority of the proposed approach.     

二维谱估计算法的空间探测性能分析及验证

针对二维谱估计算法的空间探测性能分析的需求,从计算速度、计算量、顽健性、计算精度以及实际工程应用的角度出发,对基于L型阵列的二维MUSIC、二维干涉仪、二维增广矩阵束的谱估计算法进行了简要介绍,并对上述二维谱估计算法的性能进行了仿真分析,得到了3种算法的角度RMSE的对比分析,可知在同样仿真条件下,二维增广矩阵束算法最优,二维MUSIC算法次之,二维干涉算法最差。同时,构建了相应的试验场景,通过试验分析上述二维谱估计算法的空间探测性能,得到的试验结论与仿真结论一致。在此基础上,提出了二维增广矩阵束算法可扩展应用到雷达测控一体化系统的思路。     

互相关矩阵重排获取信号子空间的二维DOA估计算法

本文提出一种L型阵列的二维子空间DOA估计算法,该算法通过重排阵列接收数据的互相关矩阵获取信号子空间,然后根据信号子空间生成一个二维谱函数,最后通过二维搜索估计信号的来波方向。由于该算法采用二维谱峰搜索,所以不需要对俯仰角和方位角进行配对。与二维MUSIC算法相比,该算法的估计精度略有下降,但该算法不需要对矩阵做特征值分解,计算量降低且易于实现。文中给出了该算法的推导过程和具体实现步骤,并进行了实验仿真,仿真结果说明了算法的有效性。      

Cumulant-based LP method for two-dimensional spectral estimation

A cumulant-based linear prediction (CBLP) method for two-dimensional (2-D) spectral estimation is presented. The main idea of the method is to compute the coefficients of two different single-quadrant prediction filters by applying the LP theory to a selected 2-D fourth-order mixed cumulant slice of the noisy signal. These coefficients are employed in formulating two different autoregressive spectral models. Both spectral models are combined to obtain the desired spectral estimate. The effectiveness of the proposed CBLP method is demonstrated through computer simulation     

Fourier-based reconstruction for fully 3-D PET: optimization of interpolation parameters

Fourier-based approaches for three-dimensional (3-D) reconstruction are based on the relationship between the 3-D Fourier transform (FT) of the volume and the two-dimensional (2-D) FT of a parallel-ray projection of the volume. The critical step in the Fourier-based methods is the estimation of the samples of the 3-D transform of the image from the samples of the 2-D transforms of the projections on the planes through the origin of Fourier space, and vice versa for forward-projection (reprojection). The Fourier-based approaches have the potential for very fast reconstruction, but their straightforward implementation might lead to unsatisfactory results if careful attention is not paid to interpolation and weighting functions. In our previous work, we have investigated optimal interpolation parameters for the Fourier-based forward and back-projectors for iterative image reconstruction. The optimized interpolation kernels were shown to provide excellent quality comparable to the ideal sinc interpolator. This work presents an optimization of interpolation parameters of the 3-D direct Fourier method with Fourier reprojection (3D-FRP) for fully 3-D positron emission tomography (PET) data with incomplete oblique projections. The reprojection step is needed for the estimation (from an initial image) of the missing portions of the oblique data. In the 3D-FRP implementation, we use the gridding interpolation strategy, combined with proper weighting approaches in the transform and image domains. We have found that while the 3-D reprojection step requires similar optimal interpolation parameters as found in our previous studies on Fourier-based iterative approaches, the optimal interpolation parameters for the main 3D-FRP reconstruction stage are quite different. Our experimental results confirm that for the optimal interpolation parameters a very good image accuracy can be achieved even without any extra spectral oversampling, which is a common practice to decrease errors caused by interpolation in Fourier reconstruction.     

High resolution two-dimensional minimum free energy AR spectral estimation

We extend the minimum free energy (MFE) parameter estimation method to 2-D fields. This 2-D MFE method may be used to determine autoregressive (AR) model parameters for spectral estimation of 2-D fields. It may also be used to provide AR models for texture synthesis. The performance of the technique for closely spaced sinusoids in white noise is demonstrated by numerical example. Better results can be achieved than with the multidimensional Levinson algorithm.     

Motion-based grouping of optical flow fields: the extrapolation andsubtraction technique

The goal of three-dimensional (3-D) motion segmentation is to identify the image areas projected by different moving objects in 3-D space. However, many prevailing methods merely detected the discontinuity of optical flow field and usually considered these boundaries as that produced by different 3-D motions. In fact, the flow discontinuity can be generated either by two different 3-D motions or by the structural discontinuity on the same moving object. The wrong identification causes several problems in 3-D motion estimation. A simple method called the extrapolation and subtraction (ES) technique is proposed to solve these problems. The input image flow field is first partitioned into several functionally analytic regions. Each analytic region is assumed to be projected by a roughly planar patch moving in 3-D space. Based on the parameterization of these analytic flow fields, the ES technique provides a very simple and fast method to test the 3-D motion compatibility between two interested analytic flow fields.