基于推理模型与指数加权卡尔曼滤波的链路质量估计

doi:10.19678/j.issn.1000-3428.0054810

计算机工程 ›› 2020, Vol. 46 ›› Issue (5): 216-223. doi: 10.19678/j.issn.1000-3428.0054810

基于推理模型与指数加权卡尔曼滤波的链路质量估计

夏宇¹, 刘伟¹, 罗嵘², 胡顺仁¹

1. 重庆理工大学电气与电子工程学院, 重庆 400054;
2. 清华大学电子工程系, 北京 100084

收稿日期:2019-05-05 修回日期:2019-07-18 发布日期:2019-08-06
作者简介:夏宇(1995-),男,硕士研究生,主研方向为无线传感器网络;刘伟,讲师、博士;罗嵘,副教授、博士;胡顺仁,教授、博士。
基金资助:
国家自然科学基金（61601069）；重庆市基础科学与前沿技术研究项目（cstc2017jcyjAX0254）；重庆市教委科学技术研究项目（KJ1600935）。

Link Quality Estimation Based on Inference Model and Exponentially Weighted Kalman Filtering

XIA Yu¹, LIU Wei¹, LUO Rong², HU Shunren¹

1. School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China;
2. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Received:2019-05-05 Revised:2019-07-18 Published:2019-08-06

摘要/Abstract

摘要： 现有基于链路质量指示（LQI）的估计方法未能有效解决LQI参数波动较大的问题，并且所使用的LQI与收包率（PRR）的映射关系模型没有考虑实际物理意义。为此，通过推理LQI和PRR的理论关系，建立更具实际物理意义的双曲正切模型，并提出一种链路质量估计方法。通过指数加权卡尔曼滤波获得更为稳定的LQI估计值，再利用双曲正切模型对链路质量进行定量估计。实验结果表明，该方法能够更真实地反映链路质量，与LETX、K-CCI方法相比，其估计误差在不同质量链路下降低了11.21%~52.26%。

关键词: 无线传感器网络, 链路质量估计, 链路质量指示, 指数加权卡尔曼滤波, 推理模型, 双曲正切模型

Abstract: The existing estimation methods based on Link Quality Indicator(LQI) fail to effectively solve the problem of large fluctuation of LQI parameters.Besides,the mapping relationship model between LQI and Packet Receiving Rate(PRR) does not consider the actual physical meaning.Therefore,by inferring the theoretical relationship between LQI and PRR,this paper establishes a hyperbolic tangent model with more actual physical meaning,and proposes a link quality estimation method.On the basis of exponentially weighted Kalman filtering,a more stable LQI estimation is obtained,and then the link quality is quantitatively estimated by the hyperbolic tangent model.Experimental results show that the proposed method can reflect the link quality rather factually.Compared with the LETX and K-CCI method,the estimation error of this method is reduced by 11.21%~52.26% in different quality links.

Key words: wireless sensor network, link quality estimation, Link Quality Indicator(LQI), exponentially weighted Kalman filtering, inference model, hyperbolic tangent model

中图分类号:

TP393

夏宇, 刘伟, 罗嵘, 胡顺仁. 基于推理模型与指数加权卡尔曼滤波的链路质量估计[J]. 计算机工程, 2020, 46(5): 216-223.

XIA Yu, LIU Wei, LUO Rong, HU Shunren. Link Quality Estimation Based on Inference Model and Exponentially Weighted Kalman Filtering[J]. Computer Engineering, 2020, 46(5): 216-223.

http://www.ecice06.com/CN/Y2020/V46/I5/216

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