一种自相似业务量预测的卡尔曼滤波算法

郭庆; 那振宇; 顾学迈

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一种自相似业务量预测的卡尔曼滤波算法

更新时间：2025-07-09

- 一种自相似业务量预测的卡尔曼滤波算法
- A Kalman Filtering Algorithm for Self-Similar Traffic Prediction
- 西安交通大学学报 2009年43卷第4期页码：57-61
- 作者机构：
  
  哈尔滨工业大学通信技术研究所,哈尔滨,150001
- 作者简介：
- 基金信息：
  
  : 2008-07-08. 作者简介: 郭庆(1964-),男,教授,博士生导师. 基金项目: 国家自然科学基金资助项目(60532030)
- DOI：
  中图分类号： TN915.01
- 网络首发：2009-04-10，
  
  纸质出版：2009
- 稿件说明：
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郭庆, 那振宇, 顾学迈. 一种自相似业务量预测的卡尔曼滤波算法[J]. 西安交通大学学报, 2009,43(4):57-61.

A Kalman Filtering Algorithm for Self-Similar Traffic Prediction[J]. 2009, 43(4): 57-61.
郭庆, 那振宇, 顾学迈. 一种自相似业务量预测的卡尔曼滤波算法[J]. 西安交通大学学报, 2009,43(4):57-61. DOI：

A Kalman Filtering Algorithm for Self-Similar Traffic Prediction[J]. 2009, 43(4): 57-61. DOI：

摘要

针对网络拥塞控制中不能准确预测自相似业务量的问题

提出了一种噪声在线估计卡尔曼滤波(NOEKF)算法.NOEKF算法不依赖于业务源反馈信息

通过观测节点处当前和过去时刻的业务量来预测下一时刻的业务量

并建立了业务量的卡尔曼滤波状态方程和观测方程

给出了递推形式的状态向量最佳估计形式.考虑到未知状态方程和观测方程噪声的统计特性

采用在线估值法

并引入遗忘因子对噪声的统计特性进行估计.NOEKF算法预测准确、偏差小.仿真结果表明

与经典卡尔曼滤波算法和时间序列预测方法比较

NOEKF算法能够更精确地预测自相似业务量

预测误差可降低60%以上.

Abstract

A noise on-line estimation Kalman filtering(NOEKF)algorithm is presented to deal with the inaccurate self-similar traffic prediction in network congestion control. The proposed algorithm is independent of the feedback information from traffic sources

and predicts the traffic through observing both the current and previous traffics in a node. Both the state equation and the observation equation are established

and then an optimal recursive formula for the estimation of the state vector is given. By taking the unknown noise statistics of both the state equation and the observation equation into account

an on-line estimation method with forgetting factor is used to estimate the noise statistics. Comparisons with existing algorithms show that the NOEKF algorithm has the advantages of high accuracy and minor prediction error. Simulation results show that the NOEKF algorithm predicts self-similar traffic more accurately than the classical Kalman filtering and time series prediction algorithms do

and that the prediction error is reduced by more than 60%.

关键词

Keywords

references

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