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卡尔曼滤波在数学上是一种统计估算方法，通过处理一系列带有误差的实际量测数据而得到的物理参数的最佳估算。
例如在气象应用上，根据滤波的基本思想，利用前一时刻预报误差的反馈信息及时修正预报方程，以提高下一时刻预报精度。
作温度预报一般只需要连续两个月的资料即可建立方程和递推关系。
-Kalman filter in mathematics is a statistical estimation methods, By a series of errors with the actual
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Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle
Filters (PFs) that exploit conditional dependencies between
parts of the state to estimate. By doing so, RBPFs can
improve the estimation quality while also reducing the overall
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：该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift 跟踪算法，提出了基于多
特征的Mean Shift 人脸跟踪算法以解决Mean shift 跟踪算法的鲁棒性问题。通过对LTP 纹理特征的分析、研究，
提出了一个LTP 关键纹理模型，既增强了目标的关键纹理信息，又简化了LTP 纹理模型。在此基础上，提出一
种基于LTP 关键纹理特征和肤色特征的Mean Shift 人脸跟踪算法，有效地解决了Mean Shift 算法的鲁棒
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本程序是关于四元数法捷联惯导解算算法的matlab程序组合包，其中包括滤波初始对准仿真，罗经法初始对准仿真，捷联惯导解算仿真，组合卡尔曼滤波等演示程序及其必需的参数矩阵转换程序，程序算法皆是本人通过大量阅读捷联惯导经典论文书籍编写的，经过调试已经通过，所得圆锥误差，划桨误差与秦永元所编惯性导航一书相符，可靠性较高。适合惯导学习者参考改进使用
-This procedure is the law about quaternion SINS algorithm matlab solver co
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基于多目标的跟踪和实现，里面介绍了跟踪算法的现状，以及如何基于mean shift和卡尔曼滤波改进跟踪算法-Based on multi-target tracking and realization, which describes the status of the tracking algorithm, and how mean shift and Kalman filter based tracking algorithm to improve
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基于卡尔曼算法的AR模型系数预测，利用卡尔曼滤波算法对AR模型的系数进行实时更新，可以观察到预测准确度有明显提高-Kalman algorithm based on AR model coefficients predicted using the Kalman filter AR model coefficients for real-time updates can be observed significantly improve prediction accuracy
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UKF算法及其在纯方位目标跟踪中的应用，改进 无迹卡尔曼滤波-UKF algorithm and its only target tracking application, unscented Kalman filter to improve
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无线传感器网络定位技术中可以用卡尔曼滤波来改善定位精度-the Kalman filter can be used to improve the positioning accuracy of location in wireless sensor networks
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利用kalman滤波实现GPS定位精度的提高-GPS positioning using kalman filter to improve the accuracy achieved
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卡尔曼滤波基本算法的matlab程序，最近一直在研究此类问题，程序准确无误，方便初学者学习研究，并以此为基础进行改进~-Kalman filter algorithm matlab program, has recently been in the study of such problems, the program is accurate, easy for beginners to learn research and improve as a basis to
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用多尺度卡尔曼滤波法，对信号参数进行识别估计。高频信号和低频信号识别结合起来改进了算法识别的精确度和准确度。-It is an implementation of hierarchical (a.k.a. multi-scale) Kalman filter using belief propagation. The model parameters are estimated by expectation maximization (EM) algorithm. In this impleme
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结合kalman滤波的meanshift跟踪算法，meanshift算法是一种经典的均值漂移跟踪算法，采用颜色直方图作为跟踪特征，具有很高的稳定性和鲁棒性，kalman滤波是一种线性滤波预测算法。在meanshift算法中引入kalman滤波，可以提高算法的稳定性，同时提高算法执行效率。代码中除了avi格式视频读取采用opencv库函数外，核心算法均为纯C++实现，注释详细，很容易理解，同时具有很高的参考价值。 -Meanshift kalman filtering combined track
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This paper deals with using of low-cost Global
Navigation Satellite System (GNSS) sensors in a localization
process for an autonomous guidance system of mobile robots.
Generally, this process is made using a Kalman Filter (KF) to
fuse informa
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HSF processes the distorted image ˜ x by M filters chosen to improve the image quality for regions with different characteristics. At each pixel,
a feature vector yn is computed using local image data. The feature vector is then used to determi
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This file compares three different versions of the Kalman filter.
The Kalman filter is used for recursive parameter estimation.
The Kalman filter can handle noisy measurements.
The first implemented filter (fcn_KF) is the Kalman filter with
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用α-β滤波器结合卡尔曼滤波器对相位差及其变化率数据进行处理,提高数据的测量精度,从而为进行机载单站无源定位提供高精度数据.给出了误差均方差作为精度指标.-Combined with the α-β filter Kalman filter and the rate of change of phase data processing, improve the measurement accuracy of the data, thus providing high-precision data
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该算法使用扩展卡尔曼滤波提议的分布模型，来改进粒子滤波-The algorithm uses the extended Kalman filter distribution model proposed to improve particle filter
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利用交互多模型无迹卡尔曼滤波算法实现机动目标跟踪。-Interacting multipl emodel Unscented kalman filter （IMM UKF)is presented to improve the performance of target tracking.
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基于卡尔曼滤波的电力负荷预测算法，针对预测效果不佳，提出了增加改进因子，以提高算法的精度-Power load forecasting algorithm Kalman filter, the prediction results are poor, improvement factor proposed increase, to improve the accuracy of the algorithm
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the code is used to the failure detection and predection of tool wear with Kalman Filter model(calculus of twice differences in order to improve better estimation precision.
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