基于奇异值分解的特征跟踪方法

在传统的基于模板匹配的跟踪方法中，均是给定一个模板，然后从图像中各个位置取出一个个与模板大小一致的区域进行相似性度量，找出与模板距离最小的一个区域作为当前模板，以便进行下一步的匹配跟踪工作。在景象匹配和相关跟踪过程中，由于所面临的大多数是变化的场景，实时获取的图像与预存模板之间存在比较大的差异，传统相关匹配方法的应用就会受到限制；而且在跟踪过程中，随时更新模板会造成跟踪性能对扰动过分敏感，从而产生漂移。首先拍摄目标不同角度的图像（尽可能包含目标可能出现的所有情况），构成目标图像训练集合，抽取出特征矩阵，对它进行奇异值分解，构成一个关于目标的多维空间。然后再用匹配方法在全局范围搜索，找出目标的大致位置，并利用收敛方法在确定的大致位置内进行搜索，确定目标的仿射变换系数，从而得到一个目标位置的确切描述。     

强激光热斑形成及其稳定性研究

在论述热点跟踪及其传热机理时将靶材的比热溶、热传导系、在激光的热藕合系数均考虑成温度和时间的函数，又考虑靶材在运动时将激光辐照物体时热点的建立及其稳定情况作一动态数值模拟，为热点跟踪作准备。     

基于模糊聚类的半自动跟踪技术研究

现有半自动跟踪系统将操作员等效为准线性环节,增大了训练的难度和成本,不适应复杂战场环境下多个快速机动目标的选取和跟踪.利用特征提取方法得到视场的“潜在目标”集,引入模糊聚类方法,通过对操作响应进行分类,建立操控意图与目标运动特征之间的关系模型.通过充分的仿真试验,对比研究了跟踪平稳性、快速性、准确性等指标,结果说明该方法能够有效提升整体跟踪性能.     

基于反步自适应神经网络的船舶航迹控制

针对欠驱动船舶在稳定航速条件下轨迹跟踪问题,提出了一种基于自适应神经网络与反步法相结合的控制算法.该算法将实际的欠驱动船舶视为模型完全未知的非线性系统,利用神经网络的函数逼近特性实现控制器中非线性部分的在线估计,采用同时调整输入层-隐层、隐层-输出层间的权值阵的方法进行神经网络权值调整.通过选取积分型Lyapunov函数证明了闭环系统的稳定性.仿真实验表明该控制策略具有良好的跟踪特性,可以实现对期望航迹的精确跟踪.     

A variational method for contour tracking via covariance matching

This paper presents a novel formulation for contour tracking.We model the second-order statistics of image regions and perform covariance matching under the variational level set framework.Specifically,covariance matrix is adopted as a visual object representation for partial differential equation(PDE) based contour tracking.Log-Euclidean calculus is used as a covariance distance metric instead of Euclidean distance which is unsuitable for measuring the similarities between covariance matrices,because the matrices typically lie on a non-Euclidean manifold.A novel image energy functional is formulated by minimizing the distance metric between the candidate object region and a given template,and maximizing the one between the background region and the template.The corresponding gradient flow is then derived according to a variational approach,enabling partial differential equations(PDEs) based contour tracking.Experiments on several challenging sequences prove the validity of the proposed method.     

Joint spatial registration and multi-target tracking using an extended PM-CPHD filter

An extended product multi-sensor cardinalized probability hypothesis density (PM-CPHD) filter for spatial registration and multi-target tracking (MTT) is proposed. The number and states of targets and the biases of sensors are jointly estimated by this method without the data association. Monte Carlo (MC) simulation results show that the proposed method (i) outperforms, although computationally more expensive than, the extended multi-sensor PHD filter which has been proposed for joint spatial registration and MTT; (ii) outperforms the multi-sensor joint probabilistic data association (MSJPDA) filter which is also extended in this study for joint spatial registration and MTT when the clutter is relatively dense.     

An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot

We present an iterative linear quadratic regulator(ILQR) method for trajectory tracking control of a wheeled mobile robot system.The proposed scheme involves a kinematic model linearization technique,a global trajectory generation algorithm,and trajectory tracking controller design.A lattice planner,which searches over a 3D(x,y,θ) configuration space,is adopted to generate the global trajectory.The ILQR method is used to design a local trajectory tracking controller.The effectiveness of the proposed method is demonstrated in simulation and experiment with a significantly asymmetric differential drive robot.The performance of the local controller is analyzed and compared with that of the existing linear quadratic regulator(LQR) method.According to the experiments,the new controller improves the control sequences(v,ω) iteratively and produces slightly better results.Specifically,two trajectories,’S’ and ’8’ courses,are followed with sufficient accuracy using the proposed controller.     

基于小二乘-卡尔曼滤波的wMPS系统跟踪定位算法研究

wMPS(workspace Measuring Position System)是一种新型的基于平面交会原理的网络式测量系统,在静态测量研究的基础上,研究了动态跟踪测量的问题。为了减小运动引入的测量误差,利用运动方程提高测量精度,采用了最小二乘法对接收器的位置进行静态估算,将其结果作为伪观测值,然后利用卡尔曼滤波做进一步处理,该方法避免了观测方程中的非线性误差,实现了接收器在运动过程中的高精度跟踪定位。通过试验对最小二乘法和最小二乘-卡尔曼滤波法进行了比较,结果表明最小二乘-卡尔曼滤波法的估值精度远高于最小二乘法。     

运动目标检测与跟踪的DSP实现

研究了运动目标检测与跟踪的DSP（DigitalSignalProcessor）实现算法,以形心跟踪算法为整个处理系统的核心。采用目标形心跟踪算法,通过目标分割阶段的目标标记,如目标面积、周长、形心位置等信息的提取建立目标跟踪波门,实现目标的连续跟踪,并将此算法移植到sEED—VPM642硬件平台,实验结果表明能够达到预定目标。此外,为了克服形心算法的准确性和实时性缺陷,采用粒子滤波对算法进行必要的扩展,从MATLAB的仿真结果看,除个别采样点存在误差较大的情况,真实值曲线与粒子滤波跟踪曲线拟合较好。