Description: 本文提出了有模型指导的三维人体运动跟踪框架，将一个多关节的圆台形状三维人体模型与多个视频图像中的外轮廓、边界、灰度和肤色特征进行匹配，使人体运动跟踪变成一个状态估计问题。并且，使用基于概率模型的粒子滤波算法来完成非线性、非高斯动态系统的状态估计。
-In this paper, the model has guided three-dimensional human motion tracking framework, will be more than one of the frustum of a cone shape of the joints of three-dimensional human body model with a number of video images of the outer contour, border, greyscale and color matching features, so that human motion tracking into a state estimation problem. In addition, the use of probabilistic model based particle filtering algorithm to complete the non-linear, non-Gaussian dynamic system state estimation. Platform: |
Size: 6459392 |
Author:阳关 |
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Description: EKF_PF 基于扩展kalman的粒子滤波 可解决非线性状态估计问题-EKF_PF based on extended kalman particle filter to address the issue of non-linear state estimation Platform: |
Size: 5120 |
Author:fortune |
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Description: 该程序用扩展的Kalman滤波实现可非线性状态的估计。-With the expansion of the program realization of the Kalman filter may be non-linear state estimation. Platform: |
Size: 1024 |
Author:liufu |
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Description: EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精度。
-EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy. Platform: |
Size: 130048 |
Author:zyz |
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Description: 连续搅拌反应釜CSTR系统是聚合反应工业中广泛使用的重要系统，它是一个很强的非线性系统，其系统建模、状态估计和实时控制等问题，-Continuous stirred tank reactor CSTR polymerization system is widely used in industry-critical systems, it is a very strong non-linear system, the system modeling, state estimation and real-time control problems, Platform: |
Size: 35840 |
Author:苏洋 |
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Description: SVM Multiregression for Non Linear
Channel Estimation in Multiple-Input
Multiple-Output Systems
在多输入多输出系统中的SVM多元回归非线性逼近-This paper addresses the problem of Multiple-Input Multiple-Output (MIMO) frequency non-selective
channel estimation. We develop a new method for multiple variable regression estimation based on Sup-
port Vector Machines, a state-of-the-art technique within the machine learning community for regression
estimation. We show how this new method, that we call M-SVR, can be effi ciently solved. The pro-
posed regression method is evaluated in a MIMO system under a channel estimation scenario, showing
its benefi ts in comparison to previous proposals when non linearities are present in either the transmitter
or the receiver sides of the MIMO system. Platform: |
Size: 120832 |
Author:lux |
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Description: 粒子滤波是基于递推的蒙特卡罗模拟方法的总称，可用于任意非线性，非高斯随机系统的状态估计。-The particle filter is based on recursive Monte Carlo simulation method general, can be used for any non-linear, non-Gaussian random system state estimation. Platform: |
Size: 339968 |
Author:冯浩 |
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Description: 著名的卡尔曼滤波器，植根于状态空间线性动力学系统，对线性递归最优滤波问题提出了一个叠代的解决方法。它不仅适用于平稳的环境，而且还适用于非平稳的环境。其估算结果是通过前一次的估计值与新的信息来计算更新的状态值；所以只有先前的估计值需要存储空间。卡尔曼滤波器采用了更高效的线性估计,比过去需要通过计算整个过滤过程中的每一个步骤更有效率。-The well-known Kalman filter, rooted in the state-space linear dynamical systems, an iterative solution of linear recursive optimal filtering problem. It applies not only to the stable environment, but also for non-stationary environment. The estimation result is calculated by the previous estimated value with the new information to update the state value require storage space, so that only the previously estimated values. The Kalman filter uses a more efficient linear estimation, than in the past need to be calculated in the entire filtration process every step more efficient. Platform: |
Size: 67584 |
Author:万达 |
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Description: State estimation in non-linear markov jump systems with uncertain switching probabilities Platform: |
Size: 252928 |
Author:gaofei |
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Search - non linear state estimation