基于卡尔曼滤波预测的无偏量测转换方法

针对在目标跟踪系统中通常使用量测转换方法将球面坐标系下的量测量转换到笛卡尔坐标系下,传统量测转换方法在互距离测量误差增大时跟踪性能有所下降的问题,提出一种基于卡尔曼滤波预测的无偏量测转换方法。采用无偏量测转换方法变换量测量,基于卡尔曼滤波预测值,结合无迹变换算法估计转换量测方差。仿真结果表明,所提出的转换量测卡尔曼滤波算法较现有方法具有更高的跟踪精度和可信度。     

联合ADS-B的最小二乘雷达系统误差估计方法

为了解决两坐标雷达系统误差估计问题,提出一种联合ADS-B的最小二乘雷达系统误差估计方法.提出方法首先将ADS-B量测从地理坐标系转换到雷达局部直角坐标系,建立统一的配准空间;其次以雷达航迹的采样时间为基准,对ADS-B航迹进行插值,构造新的ADS-B航迹;然后采用直线拟合算法分别计算雷达航迹和ADS-B航迹的直线方程,并计算两条直线的夹角,再利用该夹角补偿雷达航迹的航向角数据;最后采用最小二乘算法估计雷达系统误差.实测数据实验结果表明,与传统直线拟合方法和最小二乘方法相比,提出方法能够更有效地估计雷达系统误差;经过提出方法配准处理后,雷达航迹数据的平均斜距离误差和方位角误差分别降低71.7%和52.7%.     

基于着陆雷达的飞机下滑航迹融合算法设计与实现

针对目前飞机进近着陆阶段部分型号着陆雷达实时提供三维位置数据时信息不完整的问题,提出了一种基于雷达坐标系的航迹融合算法,将不同源的航迹数据转换到雷达坐标系下进行航迹关联,再进行最小二乘加权卡尔曼滤波,并通过仿真验证与工程实践,证明该算法可以有效实现飞机着陆阶段的航迹融合。     

啤酒瓶字符的圆周投影特征提取与识别

针对已分割完成的啤酒瓶凸性字符,提出了一种基于圆周投影变换的图像特征提取方法。以质心为极点将笛卡尔坐标系变换为极坐标系,进行圆周投影计算,将二维的字符图像变换为一维投影。利用小波变换（WT）和离散余弦变换（DCT）,提取不同字符的有效特征。通过相同、相似字符特征提取实验,表明该方法具有较好的抗噪声能力和分类性能。     

基于对数极坐标下的SVD数字水印算法

为了解决笛卡尔坐标系下水印不同步问题文章提出了一种对数极坐标系下的基于奇异值分解的抗旋转,缩放攻击鲁棒性水印算法。对于旋转,缩放造成的几何攻击,我们可以通过对数极坐标系将笛卡尔坐标系中的旋转,缩放变换转换为循环平移的性质,对于循环平移后的图像,在水印检测时我们可以使用穷举遍历的方法来检测测试图像是否包含水印信息。实验结果证明,该方法可以获得良好的图像视觉效果,同时对于加噪,滤波,JPEG压缩,剪切攻击也具有有很好的鲁棒性。     

机器人转动惯性张量的坐标变换

转动惯性张量是机器人动力学计算中的一个重要的物理量,本文用严格的数学方法推导并证明了一个刚体在两个共原点坐标系间转动惯性张量的变换公式,从而得出刚体从某一特定坐标系中的转动惯性张量来计算其它共原点坐标系中转动惯性张量的计算方法.     

被动传感器航迹起始算法

针对目标数目未知和被动传感器测量精度不同情况下的航迹起始问题,提出一种基于多站被动传感器系统的航迹起始算法.该算法首先利用多规则法对测量数据进行预处理,消除一部分“鬼点”,然后将模糊集的思想引入Hough变换,利用模糊函数的隶属度作为参数空间累积量,最后通过序列Hough变换检测航迹.仿真实验表明:该算法可有效改善航迹簇拥现象,抑制虚假航迹的产生,适合于杂波环境下目标的航迹起始.     

一种不同坐标系之间的变换矩阵的转换方法

在三维图形的研究和开发过程中,由于不同三维图形系统所使用的坐标系不统一,造成了在一个坐标系中的模型不能直接引入到别的坐标系中,针对野战战术三维环境原型系统ＢＩＴ－ＶＢＦＳ的开发过程中所遇到的将模型从右手坐标移植到左手坐标系的问题,提出了一种在不同三维坐标系之间寻找对应的变换矩阵的一般性方法,这种方法得到的是对于变换的转换矩阵,所以在对模型进行实际的转换时并没有增加计算的复杂度。     

基于刚体运动学的坐标系配准理论及算法

为避免公共点的误差及不一致性对坐标系转换精度的影响,研究了一种基于刚体运动理论的坐标系配准方法.将公共点组合成刚体,根据刚体上的点相互之间位置关系在两个坐标系下不变的特征,采用最小二乘法求解出最优变换矩阵.指出公共点空间布局方法对转换精度的影响,研究并实现了剔除超差公共点的算法.实验结果证明了该方法的正确性,为高精度测量的坐标系转换提供了理论依据.     

基于分块DCT变换的抗旋转,缩放攻击零水印算法

大多数基于DCT、变换域的零水印算法没有抵抗几何攻击的能力,例如将图像旋转微小角度即可导致水印检测的失败.为了提高基于DCT变换域的零水印算法抵抗图像抗旋转,缩放攻击的能力,本文提出了一种基于分块DCT变换的抗旋转,缩放攻击零水印算法.对于旋转,缩放造成的几何攻击,我们可以通过对数极坐标系将笛卡尔坐标系中的旋转,缩放变换转换为循环平移的性质,对于循环平移后的图像,在水印检测时我们可以使用穷举遍历的方法来检测测试图像是否包含水印信息.实验结果证明,该方法可以获得良好的图象视觉效果,同时对于加噪,滤波,JPEG压缩,剪切攻击也具有有很好的鲁棒性.     

同归约变换结合的自动单模变换技术

针对应用自动单模变换的两大困难：如何自动找出使多重循环并行化的恰当的单模变换矩阵以及如何解决妨碍单模矩阵计算的非常数归约相关距离,提出了如何对给定常数距离矩阵,自动找出使循环并行化的恰当的单模变换矩阵的技术;然后提出将数组归约相关表示为最小常数距离向量,从而使存在归约相关的多重循环也能够应用自动单模变换技术,为自动单模变换技术走向实用化提供了理论依据．     

一种高阶无迹卡尔曼滤波方法

现有的研究中,高阶无迹变换（Unscented transform,UT）还不存在具体的解析解,因此,无法利用高阶无迹变换获得具备更高精度的高阶无迹卡尔曼滤波器（Unscented Kalman filter,UKF）.为了解决这一问题,本文在五阶容积变换（Cubature transform,CT）的基础上,通过引入一个自由参数κ,得到高阶无迹变换的解析解,从而获得了高阶无迹卡尔曼滤波器（Unscented Kalman filter,UKF）.同时验证了现有的五阶容积变换和五阶无迹变换分别是本文所提出的高阶无迹变换在κ=2和κ=6-n时的两个特例.进而分析和讨论了高阶无迹卡尔曼滤波器在系统不同维数条件下κ值的最优选取,并讨论了其稳定性.纯方位跟踪模型和弹道目标再入模型仿真验证了本文方法的正确性,且与现有方法相比具有更高的精度.     

UKF与EKF在空对海单站TO-TMA中的应用

探讨了目标运动分析(Target moving analysis,TMA)中基本的非线性估计问题,介绍了基于UT的UKF 算法的设计思想与具体实现,特别针对空对海单站无源到达时间TMA(TO-TMA)问题应用UKF和EKF进行了对照研究,建立了问题的离散非线性滤波估计模型,设计了典型的应用场景,给出了Monte Carlo仿真运行结果;表明UKF在该特定应用背景下,由于模型的非线性较弱,使得UKF在精度上与EKF相当,而且在运算量上也有所增加.     

无味变换与无味卡尔曼滤波

综述了非线性估计问题的由来、无味变换（UT,Unscented Transformation）的基本思路与基本算法、各种衍变形式、σ点集的设计原则、无味卡尔曼滤波（UKF,Unscented Kalman Filtering）的基本算法及其各种改进算法、UT的本质、UKF与几种免微分非线性滤波方法的比较、UT与UKF的相关应用、针对几种UKF算法的仿真实例,以及目前在UT与UKF的研究中尚存在的一些问题和对今后研究的展望等;提出了笔者的一些最新研究成果和见解。     

Camera motion estimation by tracking contour deformation: Precision analysis

An algorithm to estimate camera motion from the progressive deformation of a tracked contour in the acquired video stream has been previously proposed. It relies on the fact that two views of a plane are related by an affinity, whose six parameters can be used to derive the six degrees-of-freedom of camera motion between the two views. In this paper we evaluate the accuracy of the algorithm. Monte Carlo simulations show that translations parallel to the image plane and rotations about the optical axis are better recovered than translations along this axis, which in turn are more accurate than rotations out of the plane. Concerning covariances, only the three less precise degrees-of-freedom appear to be correlated. In order to obtain means and covariances of 3D motions quickly on a working robot system, we resort to the Unscented Transformation (UT) requiring only 13 samples per view, after validating its usage through the previous Monte Carlo simulations. Two sets of experiments have been performed: short-range motion recovery has been tested using a Staübli robot arm in a controlled lab setting, while the precision of the algorithm when facing long translations has been assessed by means of a vehicle-mounted camera in a factory floor. In the latter more unfavourable case, the obtained errors are around 3%, which seems accurate enough for transferring operations.     

Comparing Inspections and User Testing for the Evaluation of Virtual Environments

This article describes an experiment comparing three Usability Evaluation Methods: User Testing (UT), Document-based Inspection (DI), and Expert Inspection (EI) for evaluating Virtual Environments (VEs). Twenty-nine individuals (10 end-users and 19 junior usability experts) participated during 1 hr each in the evaluation of two VEs (a training VE and a 3D map). Quantitative results of the comparison show that the effectiveness of UT and DI is significantly better than the effectiveness of EI. For each method, results show their problem coverage: DI- and UT-based diagnoses lead to more problem diversity than EI. The overlap of identified problems amounts to 22% between UT and DI, 20% between DI and EI, and 12% between EI and UT for both virtual environments. The identification impact of the whole set of usability problems is 60% for DI, 57% for UT, and only 36% for EI for both virtual environments. Also reliability of UT and DI is significantly better than reliability of EI. In addition, a qualitative analysis identified 35 classes describing the profile of usability problems found with each method. It shows that UT seems particularly efficient for the diagnosis of problems that require a particular state of interaction to be detectable. On the other hand, DI supports the identification of problems directly observable, often related to learnability and basic usability. This study shows that DI could be viewed as a “4-wheel drive SUV evaluation type” (less powerful under certain conditions but able to go everywhere, with any driver), whereas UT could be viewed as a “Formula 1 car evaluation type” (more powerful but requiring adequate road and a very skilled driver). EI is found (considering all metrics) to be not efficient enough to evaluate usability of VEs.     

Non-symmetric unscented transformation with application to in-flight alignment

To overcome the extended Kalman Filter (EKF) limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformation. However, the ordinary UT has both local and global sampling problems. In this paper, non-symmetric unscented transformation (NSUT) is proposed to solve these problems. The sigma point sets of the NSUT are non-symmetrically located with respect to the zeroth sigma point set which is equal to the prior mean. Then, the weights of the sigma points are derived by using some conditions for mean and covariance. The non-symmetric unscented Kalman filter (NSUKF) based on the sigma points and weights of the NSUT are applied to the in-flight alignment (IFA) with a large heading error. With a low cost MEMS IMU, the simulations are carried out at an S-type trajectory to analyze the performance of the proposed method.     

平板导电结构缺陷脉冲涡流和超声复合检测方法

面向平板导电结构不同深度缺陷检测需求,针对脉冲涡流和超声单一检测方法能力受限,即脉冲涡流对深层缺陷检测能力降低与超声对表面和近表面缺陷检测效果不佳的问题,提出利用两传感器信息互补的Dempster-Shafter(D-S)证据理论复合检测方法.针对脉冲涡流和超声两种检测方式适用检测区域不同而引起的证据冲突问题,研究加权分配方法加以解决.对于单传感器检测过程中可能存在误报情况的问题,研究将实际误报率考虑在内的贝叶斯推理方法以求得单一传感器检测结果的基本概率分配函数并作为D-S证据.将带有不同深度缺陷的平板导电结构作为实验对象,通过单一传感器检测、贝叶斯估计、D-S证据理论方法进行不同深度位置的缺陷检测,结果表明,使用引入加权分配的D-S证据推理方法时,缺陷检测准确性和检测范围均有所提高.     

Multiperson interaction recognition in images: A body keypoint based feature image analysis

Most interaction recognition approaches have been limited to single‐person action classification in videos. However, for still images where motion information is not available, the task becomes more complex. Aiming to this point, we propose an approach for multiperson human interaction recognition in images with keypoint‐based feature image analysis. Proposed method is a three‐stage framework. In the first stage, we propose feature‐based neural network (FCNN) for action recognition trained with feature images. Feature images are body features, that is, effective distances between a set of body part pairs and angular relation between body part triplets, rearranged in 2D gray‐scale image to learn effective representation of complex actions. In the later stage, we propose a voting‐based method for direction encoding to anticipate probable motion in steady images. Finally, our multiperson interaction recognition algorithm identifies which human pairs are interacting with each other using an interaction parameter. We evaluate our approach on two real‐world data sets, that is, UT‐interaction and SBU kinect interaction. The empirical experiments show that results are better than the state‐of‐the‐art methods with recognition accuracy of 95.83% on UT‐I set 1, 92.5% on UT‐I set 2, and 94.28% on SBU clean data set.     

Maximum correntropy unscented filter

The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilising a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimator leads to the well-known unscented Kalman filter (UKF). Although the UKF works very well in Gaussian noises, its performance may deteriorate significantly when the noises are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises. To improve the robustness of the UKF against impulsive noises, a new filter for non-linear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF). In MCUF, the UT is applied to obtain the prior estimates of the state and covariance matrix, and a robust statistical linearisation regression based on the maximum correntropy criterion is then used to obtain the posterior estimates of the state and covariance matrix. The satisfying performance of the new algorithm is confirmed by two illustrative examples.