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Description: 基于卡尔曼滤波器的交通流量分析系统，输入方式采用.txt文件形式-Kalman filter based on the traffic analysis system
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Size: 1024 |
Author: 川 |
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Description: matlab 仿真程序可用于kalman滤波应用。- INTRODUCTION
This example simulates distributed control of a DC-servo. The example
contains four computer nodes, each represented by a TrueTime kernel
block. A time-driven sensor node samples the process periodically and
sends the samples over the network to the controller node. The control
task in this node calculates the control signal and sends the result
to the actuator node, where it is subsequently actuated. The
simulation also involves an interfering node sending disturbing
traffic over the network, and a disturbing high-priority task
executing in the controller node
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Size: 21504 |
Author: zhangxiangbin |
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Description: 文献介绍了基于小波分析和kalman滤波的交通流量预测方法，应用组合模型进行预测，比单一模型较为精确-Literature describes the wavelet analysis and kalman filtering based on traffic forecasting methods, the application composition model to predict more accurate than a single model
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Size: 111616 |
Author: connie |
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Description: 基于卡尔曼方法的预测短时交通流量的程序实现-To achieve based on the the short-time traffic flow of the the the forecast of the Kalman method procedures
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Size: 1024 |
Author: 平亦超 |
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Description: 基于卡尔曼滤波的交通流预测代码，内有详细的matlab代码例子-Kalman filtering based traffic flow prediction code, with detailed matlab code examples
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Size: 5120 |
Author: xyy |
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Description: 通过使用卡尔曼滤波方法，过滤通信信号，得到有用信号特征。常用于通信领域-By using the Kalman filter, filter traffic signals to give a useful signal characteristics. Commonly used in the field of communication
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Size: 12288 |
Author: 吕佳和 |
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Description: Square Root Cubature Kalman Filter-Kalman Filter Algorithm for
Intelligent Vehicle Position Estimate
Jianmin Duan, Hui Shi*, Dan Liu, Hongxiao Yu
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
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Size: 264192 |
Author: Gomaa Haroun |
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Description: 一种综合多种算法的车辆检测和追踪方法，运行时间较长，但效果很棒(We implement a system for vehicle detection and
tracking from traffic video using Gaussian mixture models and
Bayesian estimation. In particular, the system provides robust
foreground segmentation of moving vehicles through a K-means
clustering approximation as well as vehicle tracking correspon-
dence between frames by correlating Kalman and particle filter
prediction updates to current observations through the solution
of the assignment problem. In addition, we conduct performance
and accuracy benchmarks that show about a 90 percent reduc-
tion in runtime at the expense of reducing the robustness of
the mixture model classification and about a 30 percent and 45
percent reduction in accumulated error of the Kalman filter and
particle filter respectively as compared to a system without any
prediction.)
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Size: 6215680 |
Author: O(∩_∩)O哈哈噢
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