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Knowledge of the process noise covariance matrix
is essential for the application of Kalman filtering. However,
it is usually a difficult task to obtain an explicit expression of
for large time varying systems. This paper looks at an adaptive
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本文对于非线性非高斯问题，提出了一种改进扩展卡尔曼滤波（NIEKF）新方法。该方法将迭代滤波理论引入到扩展卡尔曼滤波器方法中，有效地重复利用新的测量信息，还利用Levenberg-Marquardt 方法调整预测协方差阵以保证算法具有全局收敛性。实验结果表明，所提方法具有更高的估计精度，是一种效率较高、性能较好的跟踪方法。-This non-Gaussian for nonlinear problems, an improved extended Kalman filter (NIEKF) th
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用泰勒级数展开的形式表示高动态的载波相位参数, 给出了对高动态载体和各阶频率参数
估计的四阶加权扩展卡尔漫滤波器(EKF) , 以及实现高动态跟踪滤波器必须的状态转移矩阵和动
态噪声协方差矩阵. 计算机模拟实验分析了对载波相位和各阶频率的跟踪结果.-Taylor series expansion with the form that the carrier phase high-dynamic parameters, given the high-order dynamic freq
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A matlab for detecting events in a soccer game video. Used covariance tracking to recognize ball. Used color-based segmentation to recognize players. Based on curvature of the ball trajectories and the position relationship between players, events ar
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该代码用于实现视觉目标跟踪研究中的协方差跟踪，能够把多种时空特征融合于统一的模型中，在实现视觉目标跟踪时具有较好的鲁棒性，而且其维数等于使用特征的数量，与各个特征的维数无关，因此，其计算复杂度较小，实时性较好。-This code is used to realize covariance tracking in the research field of visual object tracking of computer vision. It enables fusion of variou
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目标运动和卡尔曼跟踪的仿真程序，给出了系统状态转移矩阵和测量过程，以及协方差和增益。通过绘图得出仿真轨迹和真实轨迹的平均误差。有助于研究目标航迹跟踪-Target motion and Kalman tracking simulation program, the system state transition matrix and measurement process, as well as the covariance and gain. Obtained by drawing the a
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这个程序是用matlab实现的车辆跟踪。用到的算法是协方差，包括梯度，rgb颜色，边缘，距离等多种元素。-This program is a vehicle tracking using matlab. The algorithm used is the covariance, including gradient, rgb color, the edge from the other elements.
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针对全球定位系统( GPS) 的微弱信号跟踪容易失锁问题, 提出一种新的跟踪方法. 利用平方根卡尔曼滤波算法, 在运算过程中运用误差协方差矩阵的平方根形式进行迭代, 有效减小了舍入误差带来的影响. 提出了一种新的GPS 信号跟踪测量模型, 在连续积分的基础上, 加入了非连续积分过程, 通过有效处理, 避免了导航信息位翻转对信号相关运算产生的影响-Weak signal tracking for the Global Positioning System (GPS), easy to loss o
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2011年IEEE T.的关于Real-Time Probabilistic Covariance Tracking With Efficient Model Update，请大家分享-2011 IEEE T. on Real-Time Probabilistic Covariance Tracking With Efficient Model Update, please share
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The extended Kalman filter (EKF) has been widely
used as a nonlinear filtering method for radar tracking
problems. However, it has been found that in
case cross-range measurement errors of the target position
are large, the performance of the
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The application uses the approach introduced in paper Covariance Tracking using Model Update Based on Means on Riemannian Manifolds , F.Porikli, O.Tuzel, P.Meer.
The tracking is based on:
1) initializing the target region
2) constructing the
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协方差跟踪，将视频变成帧读入，计算协方差矩阵，并通过遍历找到最相似点区域进行跟踪，并生成轨迹-The covariance tracking, the video frame into read, calculate the covariance matrix, and find the similarities by traversing the area for tracking, trajectory generation and
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扩展卡尔曼滤波算法C语言实现：实现对二维状态变量的预测及跟踪。包括卡尔曼滤波初始化函数，负责初始化状态变量的值，过程噪声，测量噪声，以及状态转移矩阵。 卡尔曼滤波函数负责对状态变量进行迭代滤波，整个过程包括状态预测，协方差预测，测量预测，计算卡尔曼增益，状态量更新，协方差更新。(To realize the prediction and tracking of two dimensional state variables. It includes the initialization func
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