交通运输系统工程与信息

交通运输系统工程与信息 ›› 2013, Vol. 13 ›› Issue (1): 43-.

• 智能交通系统与信息技术 • 上一篇    下一篇

基于提升小波变换的GPS动态滤波新算法

李红连*1,2，方红1，唐炬*2，张军3   

  1. 1.成都大学 电子信息工程学院，成都 610106； 2.重庆大学　电气工程学院，重庆　400044；3.重庆工商大学　 商务策划学院，重庆 400067
  • 收稿日期:2012-09-05 修回日期:2012-09-21 出版日期:2013-02-25 发布日期:2013-03-04
  • 作者简介:李红连（1973-），男，重庆忠县人，博士后，副教授.
  • 基金资助:

    国家自然科学基金（11205022）；国家社会科学基金（10ＸＧＬ００１３）；重庆市自然科学基金（CSTC2008BB0327）；四川省科技支撑计划（2012GZX0083）；四川省教育厅科技项目（12ZB170）.

GPS Dynamic Filter Algorithm Based on Lifting Wavelet Transformation

LI Hong-lian1,2, FANG Hong1, TANG Ju2, ZHANG Jun3   

  1. 1. School of Electric and Information Engineering, Chengdu University, Chengdu 610106,China; 2. School of Electronic Engineering,Chongqing University, Chongqing 400044, China; 3. School of Strategical Planning, Chongqing Technology and Business University, Chongqing 400067, China
  • Received:2012-09-05 Revised:2012-09-21 Online:2013-02-25 Published:2013-03-04

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被引次数

23

摘要：

针对应用卡尔曼滤波器进行车辆GPS导航信号的动态滤波时难以建立精确的数学模型以及传统小波变换在实时性方面存在不足，提出了基于提升小波变换的GPS动态滤波新算法.该算法采用提升小波变换对车辆GPS导航信号进行分解；用3σ准则和多项式插值方法对各层提升小波变换系数进行粗差探测与数据修复；采用模平方软阈值去噪算法对各层提升小波变换系数进行去噪；最后进行提升小波逆变换，从而实现车辆GPS导航信号的动态滤波。仿真实验结果表明，该算法的导航定位精度优于卡尔曼滤波算法；虽然在导航定位精度方面稍比传统小波变换算法的性能高，但比传统小波变换算法速度快一倍；显然该算法对车辆GPS导航系统是有效的.

关键词: 智能交通；动态滤波；提升小波变换；车辆GPS导航系统；3&sigma, 准则

Abstract:

It is difficult to establish a precise mathematical model of dynamic filtering for vehicle GPS navigation with Kalman filtering. And the traditional wavelet transformation has some shortness in real-time processing. This paper proposes a GPS dynamic filter algorithm based on the lifting wavelet transformation (LWT). It first decomposes the vehicle GPS navigation signal with the LWT. Then,it detects, eliminates and corrects the signal’s gross error at different LWT resolution levels with statistical 3σrule and polynomial interpolation method. The signal’s noise is de-noised at different LWT resolution levels with modulus square soft-threshold de-noising method. Finally, the algorithm realizes real-time dynamic filtering for the vehicle GPS navigation system through reconstructing the de-noised LWT coefficients. The simulation results show that the algorithm is more effective than the Kalman filter in positioning precision. Compared with the traditional wavelet transformation, the speed is doubled for dynamic filtering even though the proposed algorithm only has little advantages on the positioning accuracy. Therefore, the algorithm is available for vehicle GPS navigation system.

Key words: intelligent transportation, dynamic filter, lifting wavelet transformation (LWT), vehicle GPS navigation system, 3σrule

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