Description: 信号分析与预测的卡尔曼滤波算法，VC++源程序-kalman filter arithmetic for signal analysis and prediction written with VC++. Platform: |
Size: 19456 |
Author:me |
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Description: matlab的卡尔曼滤波预测算法例子，希望对大家有帮助-matlab prediction algorithm of the Kalman filter example, we would like to help Platform: |
Size: 35840 |
Author:王振兴 |
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Description: 这个是卡尔曼滤波器的源码，主要用于视频序列的预测跟踪。-This is the source of the Kalman filter, mainly for video sequence tracking the prediction. Platform: |
Size: 26624 |
Author:吴金友 |
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Description: 使用Kalman滤波实现二维图像中目标的轨迹跟踪源代码
这个程序通过Kalman滤波实现目标轨迹跟踪，并预测目标下一出现的位置。它通过背景相减实现目标检测，然后把输出的结果交给Kalman滤波器，预测出下一出现的位置（红色），可以和实际出现的位置（绿色）相比较。
-The use of Kalman filtering to achieve the goal of two-dimensional image of the trajectory tracking of the source code of this process through the Kalman filter for trajectory tracking of goals and objectives of the next prediction appear. Through background subtraction to achieve target detection, and then output the results to the Kalman filter, prediction next appear (red), can occur and the actual position (green) compared. Platform: |
Size: 6144 |
Author:lcp |
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Description: 用一观测器从t=1秒开始对一个运动目标的距离进行连续地跟踪测量，假设观测的间隔为一秒钟，雷达到运动目标之间的距离为S(t)（1） 统计特性的初值为
（2）观测误差是与和均不相关的白噪声序列，并且有
（3）观测数据存放在附加的文件中（单位：m）。
要求：分析上述对象，建立系统模型，构造卡尔曼滤波器，编程计算，求：
（1） 距离S(t)的最佳估计及估计误差，
（2） 距离S(t-5)的最佳平滑及估计误差，
（3） 距离S(t+5)的最佳预测及估计误差，
（4） 对结果进行分析讨论。
-By one observer from the t = 1 PST on a moving target tracking for distance measurement, assuming that the observation interval is one second, the radar that the distance between the moving target for the S (t) (1) the statistical characteristics of the initial condition (2) observational error is not associated with white noise sequence, and (3) observational data stored in the attached document (unit: m). Requirements: Analysis of the above-mentioned object, the establishment of the system model, constructed Kalman filter, programming terms, seeking: (1) distance from S (t) the best estimate and the estimation error, (2) distance from S (t-5) the most good smoothing and estimation error, (3) distance from S (t+ 5) the best prediction and estimation error, (4) the results analyzed and discussed. Platform: |
Size: 2048 |
Author:裴海波 |
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Description: 在matlab simulink 中实现kalman滤波器设计，可以生动的演示kalman滤波在运动估计中的预测作用-Matlab simulink in achieving kalman filter design can be a vivid demonstration of kalman filtering in the motion estimation of the prediction Platform: |
Size: 12288 |
Author:冯安祥 |
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Description: 使用matlab_GUI编写，包括：扩展卡尔曼滤波，粒子滤波，去偏卡尔曼滤波和循环增益尔曼滤波的源程序，根据初始预测计算滤波值
再通过输入观测值进行卡尔曼滤波的仿真GUI界面程序-Matlab_GUI prepared to use, including: extended Kalman filter, particle filter, to the partial gain of Kalman filter and recycling thalmann source filter, according to the initial value of prediction filter observations through the input of the Kalman filter to simulation procedures GUI interface Platform: |
Size: 36864 |
Author:snow |
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Description: 基于“当前”统计模型的模糊自适应跟踪算法
我存的一篇论文，拿来与大家共享-Current statistical model needs to pre-define the value of maximum accelerations of maneuvering targets.So it
may be difficult to meet all maneuvering conditions.The Fuzzy inference combined with Current statistical model is
proposed to cope with this problem.Given the error and change of error in the last prediction,fuzzy system on-line
determines the magnitude of maximum acceleration to adapt to different target maneuvers.Furthermore,in tracking problem
many measurement equations are non-linear.Unscented Kalman filter is applied instead of extended Kalman filter.The
Monte Carlo simulation results show that this method outperforms the conventional tracking algorithm based on current
statistical model in both tracking accuracy and convergence rate.
Platform: |
Size: 80896 |
Author:dailu |
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Description: 实现了数据从文件的输入，ar模型预测，arma模型预测，卡尔曼滤波器模型预测，利用图形用户界面编写-Realized the data from the file input, ar model predictions, arma model prediction, Kalman filter model predictions, using a graphical user interface for the preparation of Platform: |
Size: 12288 |
Author:张迎 |
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Description: The Extended Kalman Filter with profile matching
has been employed to extract road maps in satellite images.
This algorithm suffers from several drawbacks
that result in its poor performance in difficult situations.
To improve its performance in those situations,
like junctions and varying road characteristics, we propose
to use the Unscented Kalman Filter which can deal
better with the nonlinearity of the road model. Additionally,
we use an approach to dissociate the system
measurements from the current state prediction of the
Kalman filter. This method removes the potential for
the instability of the algorithm. Platform: |
Size: 2364416 |
Author:kalaiarasan.R |
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Description: 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) the new method. The method of iterative filtering theory is introduced to the extended Kalman filter method, the effective measurement of repeated use of the new information, also using Levenberg-Marquardt method to adjust the covariance matrix of prediction algorithm to ensure global convergence. Experimental results show that the proposed method has higher estimation accuracy, is a high efficiency, good performance tracking methods. Platform: |
Size: 1527808 |
Author:李辉 |
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Description: 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 Platform: |
Size: 1024 |
Author:月光下的路 |
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Description: 利用新陈代谢灰色预测、样本自适应BP 神经网络和时间序列分析分别进行风电功率实时预测和日前预测，并采用熵值取权法确定组合权重，引入自控机制，构建反馈，提出组合预测法和基于时间序列的卡尔曼滤波法。研究结果表明，组合预测模型能减少各预测点较大误差的出现，而卡尔曼滤波能大幅消减原始序列的波动影响。-Use of metabolic gray forecast, sample adaptive BP neural network and time sequence analysis respectively conducted wind electric power real-time forecast and before prediction, and using the entropy take the right method to determine a combination of the right weight, the introduction of self-control mechanism, to build feedback, proposed combination forecasting method and based on the time sequence Kalman filtering method. The research results show that the combination forecasting model can reduce the prediction point error appears, the Kalman filter can significantly abatement fluctuations of the original sequence. Platform: |
Size: 1047552 |
Author:刘行 |
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Description: Implementation of Kalman filter Algorithm: Prediction and Correction. a complete example by a full description of the problem-Implementation of Kalman filter Algorithm: Prediction and Correction. a complete example by a full description of the problem Platform: |
Size: 51200 |
Author:Mustafa |
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