High accuracy measurement of unsteady flows using digital particle image velocimetry

The analysis of digital PIV data, either derived from CCD technology or through film and then scanned, typically involves two quantization steps: spatial and intensity quantization. The all-optical systems do not introduce these sources of error. For systems which make use of digital technology however, it is of crucial importance to have reliable error bounds and a sufficiently accurate estimate of particle position, taking into consideration both types of quantization. The accuracy demanded by aerodynamicists from PIV has been a major barrier to its practical application in the past. The more recent approach of using the Gaussian profile of the particle images to yield sub-pixel accurate position estimates has resulted in robust measurements being taken to an accuracy of 1/10th pixel and 1% in velocity for the in-plane velocity, in hostile industrial environments. A major problem for 3D PIV estimation has historically been that the out-of-plane velocity error was of the order of 3–4 times larger than in-plane. The out-of-plane velocity estimate can be derived from the change in the ratio of amplitude to variance—known as the depth factor—of the Gaussian form, as a particle traverses the beam profile. However, such measurements are crucially dependent not only on an accurate position estimate but also on an equally accurate estimate of the amplitude and variance. The accuracy of the Gaussian profile fit using a Nelder–Meade optimisation method as developed until now however, is not capable of providing the required accuracies. Therefore, this paper presents a development of the “locales” approach to position estimation to achieve the desired objective of high accuracy PIV measurements. This approach makes use of the fact that by considering all the possible digital representations of the Gaussian particle profile, regions of indistinguishable position can be derived. These positions are referred to as “locales”. By considering the density, distribution, and shape of these locales, the available precision can be estimated and an accurate (no worse than 0.5% error for a typical PIV image) in-plane velocity, accuracy can be obtained; while at the same time providing estimates of the depth factor with an error of approximately 0.8%. This paper describes the implementation of an efficient algorithm to provide velocity estimates to an accuracy of at least 0.5% in-plane, together with a discussion of the required constraints imposed on the imaging. The method was validated by creating a synthetic PIV image with CCD-type noise. The flow being analysed is that of flow past the near wake of a cylinder at a Reynolds Number of 140,000. This image was then analysed with the new method and the velocity estimates compared to the CFD data for a range of signal-to-noise ratios (SNR). For a realistic SNR of 5, the accuracy of the method is confirmed as being at least 0.5% in-plane. Finally, the algorithm was used to map an experimental transonic flow field of the stator trailing edge region of a full-size annular cascade with an estimated error of 0.5%. The experimental results are found to be in good agreement with a previously reported steady state viscous calculation and PIV mapping.     

基于辅助粒子滤波算法的红外目标跟踪

针对红外目标在跟踪中计算复杂的问题,构建辅助粒子滤波算法。利用贝叶斯重要性采样算法,在权值大的粒子基础上引入辅助粒子变量,然后重新定义重要采样分布函数,防止重采样后粒子概率密度变化。两次加权计算,使粒子权值比仅用重采样的粒子权值变化更稳定,采样点最接近真实状态;同时不同权值粒子的概率阈值可作为粒子滤波是否完成的判断准则。在二维平面构造红外运动目标模型中,系统为零均值高斯白噪声。仿真数据表明:该算法在x,y方向的均方误差、画面处理时间、RM SE性能上优于粒子滤波算法和重采样粒子滤波算法。     

基于高斯拟合的激光光斑中心定位算法

激光光斑中心定位是光学测量中的关键技术之一。通过对常用定位算法的分析,给出利用光斑图像中的不饱和点对光斑进行高斯拟合,并以拟合函数的幅值点作为光斑中心的方法。利用人工光斑对该算法进行验证,结果表明该算法误差远小于0.1 像素;利用一维高精度电动平移台、CCD相机、激光器等搭建测试系统,由计算机自动采集激光光斑图像并对其进行分析,实验结果表明该算法与理论分析结果的均方根误差仅为0.1 像素。     

3D particle positioning by using the Fraunhofer criterion

In particle tracking velocimetry, the necessary information is the 3D location of a given particle in space. This information can be obtained by examining the real image or by analyzing the interference fringe recorded on a digital camera. In this work, we measure the three-dimensional position of spherical particles by calculating the Central Spot Size of the interference pattern of a particle diffraction image. The Central Spot Size is obtained by combining the Continuous Wavelet transform and circle Hough transform. The Continuous Wavelet transform allow us in only one step enhanced quality of particle images and sets a threshold to select properly places where a Central Spot Size appear in order to determine its size via the Hough transform. The size and centroid of the Central Spot Size render z and x-y position of a particle image, respectively. The Central Spot Size is related to a criterion of a simplified theory given by the Fraunhofer theory in order to obtain z particle position. Our approach has been applied to simulated and experimental particle images. Simulated particle images show good agreement between actual and calculated Central Spot Size. An average relative error of 0.5% and 1.12% for x-y and z directions, respectively, was found in the analysis. Our experimental particle images were obtained from particle motion inside a channel. The quality of the particle images determines the accuracy of the calculation of the Central Spot Size of a particle image.     

Comparison of asynchronous particle swarm optimization and dynamic differential evolution for partially immersed conductor

The application of two techniques for the of shape reconstruction of a perfectly two-dimensional conducting cylinder from mimic measurement data is studied in the present paper. After an integral formulation, the microwave imaging is recast as a nonlinear optimization problem; a cost function is defined by the norm of a difference between the measured scattered electric fields and the calculated scattered fields for an estimated shape of a conductor. Thus, the shape of conductor can be obtained by minimizing the cost function. In order to solve this inverse scattering problem, transverse electric (TE) waves are incident upon the objects and two techniques are employed to solve these problems. The first is based on an asynchronous particle swarm optimization (APSO) and the second is a dynamic differential evolution (DDE). Both techniques have been tested in the case of simulated mimic measurement data contaminated by additive white Gaussian noise. Numerical results indicate that the DDE algorithm and the APSO have almost the same reconstructed accuracy.     

基于高斯混合概率假设滤波的水下目标跟踪算法

为了解决传统水下目标跟踪中目标数目估计不准确、状态估计误差增长过快的问题,提出了一种基于高斯混合概率假设滤波的水下目标跟踪算法。该算法基于双基地观测模型,采用高斯混合概率假设滤波算法处理方位和时延信息,利用粒子群算法处理多普勒频率获得矢量速度,进一步提升算法的跟踪精度。结果表明,该算法能完成在杂波环境下对目标的跟踪,相比传统的关联算法,能够有效地实现目标个数估计和抑制状态误差增长的目的。     

基于粒子群优化算法的自适应图像分割方法

提出了一种基于粒子群优化算法的图像分割新方法。粒子群优化(PSO)算法是一类随机全局优化技术,它通过粒子间的相互作用发现复杂搜索空间中的最优区域缩短了寻找阈值的时间。将PSO用于基于改进的最佳加权熵阈值法的图像分割中,试验结果表明,该方法不仅能够避免陷入局部极值,而且其速度得到了明显的改善,是一种有效的图像分割新方法。     

基于尺度不变特征变换和区域互信息优化的多源遥感图像配准

为了进一步提高遥感图像配准精度, 提出了尺度不变特征变换(SIFT)结合区域互信息优化的遥感图像配准方法. 首先利用混沌序列的随机性和遍历性, 提出一种混沌量子粒子群优化(CQPSO)算法, 在量子粒子群优化(QPSO)算法迭代陷入早熟收敛时, 采用一种新的机理引入混沌序列, 进化粒子克服早熟. 图像配准算法分为预配准和精配准两个过程. 基于SIFT算法提取特征点, 经匹配和有效地外点排除完成预配准, 然后对匹配特征点坐标进行亚像素级微调, 通过最小二乘法求得一系列匹配参数构造初始粒子群, 最后利用混沌量子粒子群优化区域互信息完成精配准, 得到最优匹配参数. 用一些标准测试函数对所提出的CQPSO和QPSO及粒子群优化(PSO)算法进行了实验比较, 另外, 对SIFT, SIFT结合PSO算法优化区域互信息, SIFT结合QPSO算法优化区域互信息和SIFT结合CQPSO算法优化区域互信息(SRC)等四种算法进行了不同分辨率遥感图像配准实验比较和不同时相遥感图像配准实验比较, 实验结果验证了所提出的CQPSO算法的优越性和SRC配准方法的有效性.     

A realistic coronary artery phantom for particle image velocimetry

Myocardial infarction results from the rupture of an atherosclerotic plaque, which occurs in response to both mechanical stress and inflammatory processes. In order experimentally observe flow into atherosclerotic coronary artery morphologies, a novel technique for molding realistic compliant phantom featuring injection-molded inclusions and multiple layers has been developed. This transparent phantom allows for particle image velocimetry (PIV) flow analysis and can supply experimental data to validate computational fluid dynamics algorithms and hypothesis.     

基于指数律分布的图像快速盲复原算法

针对现有图像盲复原迭代算法多存在耗时较长和难以保证收敛性等问题,提出一种改进的快速算法.首先根据指数律重建原始图像的频谱,然后利用原始图像和降质图像的频谱关系,采用多方向综合估计方法得到点扩散函数.多方向综合估计方法可降低估计误差,增加算法的稳定性.最后利用得到的点扩散函数和维纳滤波法进行图像重建.与现有算法的对比实验结果表明,针对适合大量成像系统的G类点扩散函数,本算法可以得到更准确的点扩散函数估计,且降低了振铃效应的影响,取得更好的图像复原效果.     

点光源哈特曼最优阈值估计方法研究

针对夏克-哈特曼波前传感器探测系统中噪声随时间及空间变化频率较快的特点,为了准确估计系统的最优阈值,根据高斯光斑与噪声的分布特性,提出一种以滑动窗口内像素均值及图像信号的局部梯度作为参数,构造关于噪声权重函数的方法,由此获得子孔径阈值的最优估计值,并详细分析了算法的基本原理和实现过程.以典型处理方法获取的阈值与理论最优阈值的误差作为评价标准,仿真和实验结果表明本文提出的阈值估计方法在不同信噪比、不同光斑大小的条件下,均能取得优于典型阈值处理方法获得的结果,且与理论最优阈值的误差小于10%.     

A novel algorithm for underwater moving-target dynamic line enhancement

When we use a traditional adaptive line enhancer (ALE) algorithm to detect an underwater moving target, there are two disadvantages: the ability to suppress colored Gaussian noise is low and the lower the SNR is, the worse the performance of the ALE algorithm. In order to greatly overcome these disadvantages, we take full advantage of the capability of higher order cumulants to alleviate the effect of colored Gaussian noise and develop a fourth order cumulant non-diagonal slice-based adaptive dynamic line enhancer (FOCNDSBADLE) algorithm and fourth order cumulant diagonal slice-based adaptive dynamic line enhancer (FOCDSBADLE) algorithm. The adaptive filtering coefficients of these algorithms are indirectly updated by the instantaneous fourth order cumulant slices. It is shown that these slices are comprised of noiseless sinusoids if the input signals are comprised of sinusoids corrupted by Gaussian noise. Therefore these algorithms are fit to handle highly colored Gaussian noise. Simulation tests are carried out using the measured data radiated by the underwater moving target. Simulation results have shown that the FOCNDSBADLE algorithm and FOCDSBADLE algorithm outperform the ALE algorithm and that the FOCNDSBADLE algorithm outperforms the FOCDSBADLE algorithm in the case of Gaussian noise.     

Reducing phase retrieval errors in Fourier analysis of 2-dimensional digital model interferograms

In order to measure the displacements of facets on a growing spherical Cu_2−δSe crystal with sub-nanometre resolution, we investigated the reliability and accuracy of standard method of Fourier analysis of digital laser interferograms. Guided by realistic experimental conditions (two-dimensional (2D) interferograms), starting from 2D model interferograms and using original custom designed Gaussian filtering window and multistage unwrapping procedure of the retrieved phase, we demonstrate for a considerable parameter range the non-negligible inherent phase retrieval error due to non-integer number of fringes within the digital image. Our results indicate an intermediate parameter range where the error is acceptably small. We introduce an algorithm modification that significantly reduces the error, especially for low and high fringe densities. In the experimentally most common case of diagonal fringes the reduced error is an order of magnitude smaller than for nearly one-dimensional case within almost entire parameter space.     

基于改进二次多项式拟合的布里渊频移快速高精度提取算法

为了在保证测量准确性的基础上提高基于布里渊散射的光纤分布式传感的实时性,对布里渊频移的快速、高精度提高算法进行了研究。实现了基于二次多项式拟合的布里渊频移提取算法和典型的基于洛伦兹、高斯、伪Voigt和Voigt模型的算法,采用光时域反射计(BOTDR)实测了一段长光纤上的布里渊谱,采用以上算法提取了对应的布里渊频移。计算结果表明,二次多项式拟合算法的计算速度明显快于以上经典算法,其计算耗时仅分别为以上经典算法的1.15%,1.80%,1.51%和0.51%,但计算误差明显大于经典算法,影响了其实际应用。以上结果与对应数值产生布里渊谱的计算结果吻合。为了提高该算法的计算准确性,系统研究了扫频范围、扫频点数、信噪比、线宽和扫频范围偏差对基于二次多项式的布里渊频移提取准确性的影响。结果表明:当扫频点数固定时随扫频范围增加布里渊频移误差先减少到最小值后逐渐增加,扫频点数固定时最佳扫频范围为1个线宽;扫频范围不变时随扫频点数和信噪比的增加布里渊频移误差分别成幂和指数规律减少;扫频范围与线宽比值不变及扫频点数不变时随线宽增加布里渊频移误差线性增大;随扫频范围偏差增加误差逐渐增大,实际用于拟合的谱信号尽量围绕布里渊频移左右对称。根据以上研究结果提出了一种用于布里渊频移快速提取的改进二次多项式拟合算法,该算法从测量得到布里渊谱中截取1倍线宽且关于最大增益对称的谱信号用于后续拟合,较之经典的谱拟合算法,改进算法不仅能大幅提高计算速度且计算准确性与经典算法相似。采用数值产生及实测布里渊谱的计算结果验证了所提出算法的有效性。提出的算法不仅能有效提高基于布里渊散射的光纤分布式传感的实时性。     

粒子场数字全息诊断中的再现算法研究

 粒子场的数字全息诊断中,良好的再现算法能够在较短的时间内给出高质量的再现像。利用标准粒子板模拟单层面的粒子场,使用大面阵CCD实现同轴数字全息记录,得到了大尺寸的数字全息图。针对4种数字全息再现算法,本文从再现图像的质量、再现全息图的大小和计算速度3个主要方面进行了比较研究,结果表明角谱算法(FFT-AS)具有再现图像背景均匀,再现结果中无物理图像压缩,可以再现大尺寸的全息图且具有较快计算速度的优点,适合于粒子场同轴数字全息图的再现计算。     

基于混合粒子滤波的水下小目标跟踪

针对水下小目标粒子滤波估计过程中“粒子贫化”引起的估计性能下降,提出了混合粒子滤波算法。该算法在常规粒子滤波算法基础上,在每一步迭代估计过程中进行量测的再次随机采样,以丰富随机粒子多样性,缓解水下小目标状态估计过程中的“粒子贫化”的影响。对算法进行了仿真分析,并将该方法用于水下小目标探测实验的数据处理,结果表明,相比于常规的粒子滤波算法,所提出的混合粒子滤波得到了误差更小且稳定的状态估计结果,有效地改善水下小目标跟踪的精度和稳健性。     

色彩再现的多光谱图像压缩

针对多光谱图像在色度高保真再现等领域的应用,为提高压缩效率,进一步存储传输,提出了WF系列编码方法,设计了APWS_RA算法,进而提出了一种低复杂度、光照稳定性好且支持跨设备再现的WF_APWS_RA压缩算法。首先研究了现有的基于光谱均方误差的小波嵌入编码原理,提出了色感应失真准则和视觉特性矩阵W;同时,优化了APWS编码算法的码率分配,形成APWS_RA编码算法;最后,结合以上技术,以色度误差准则指导编码,对视觉加权后的多光谱图像数据进行aPWS_rA编码,命名为WF_APWS_RA。WF_APWS_RA算法融合人眼视觉特性矩阵W,利用吸引力传播(Affinity Propagation)聚类挖掘加权图像的谱间冗余,小波变换去除其空间冗余,最后结合误差补偿机制和码率分配策略进行小波编码。实验表明,在相同比特率下,较现有低复杂度经典编码算法,WF系列编码方法能更有效地保留光谱中的色度信息,APWS_RA算法的重建光谱误差最小,WF_APWS_RA算法的重建色度精度优势明显。     

基于改进的二维指数熵及混沌粒子群的阈值分割

鉴于现常用的灰度级—平均灰度级二维直方图区域划分存在明显的不足,提出了基于灰度级—梯度二维直方图的指数熵阈值选取方法,给出了基于改进的二维直方图的指数熵阈值选取公式,并利用混沌粒子群优化算法寻找最佳分割阈值,采用递推方式降低迭代过程中适应度函数的计算代价。实验结果表明,与现有的有关算法相比,该方法不仅使分割后的图像区域内部更均匀、边界形状更准确、特征细节更清晰,而且使计算效率及粒子群的收敛精度得到提高。     

阵列探测器的像点亚像素定位精度

本文研究阵列探测器的像点亚像素定位精度与内插算法及像斑大小的关系，文中提出一种校正算法误差、提高定位精度的方法。上述分析和论述通过实验证明是正确的。     

星空观测图像目标拖尾的自动消除

为了实现对星空观测图像中高亮目标产生的拖尾现象进行自动消除,建立了目标拖尾自动消除系统,对该系统所采用的图像背景高斯噪声分布参数估计、目标拖尾检测以及图像灰度值校正等算法进行研究。利用直方图最小二乘曲线拟合估计星空观测图像背景高斯噪声的分布参数;利用观测图像中目标拖尾现象的特征提出一种基于统计信息的拖尾检测算法;在确定目标拖尾位置的基础上对被污染的像素进行灰度值校正;利用Visual Studio 2005开发出一套星空观测图像目标拖尾自动消除系统。实验结果表明:针对16 bit,1 0241 024的星空观测图像,单帧图像处理时间约为300 ms,拖尾现象得到消除,恒星和目标等有用信息未被破坏。本文算法基本满足后续观测图像中弱小目标检测稳定、可靠、精度高等要求。