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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
Update : 2025-03-07 Size : 1kb Publisher :

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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
Update : 2025-03-07 Size : 21kb Publisher : zhangxiangbin

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
Update : 2025-03-07 Size : 109kb Publisher : connie

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
Update : 2025-03-07 Size : 1kb Publisher : 平亦超

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
Update : 2025-03-07 Size : 5kb Publisher : xyy

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
Update : 2025-03-07 Size : 12kb Publisher : 吕佳和

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
Update : 2025-03-07 Size : 258kb Publisher : Gomaa Haroun

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.)
Update : 2025-03-07 Size : 5.93mb Publisher : O(∩_∩)O哈哈噢
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