[1]娄月新,陈圣迪,陆键,等.基于改进卡尔曼滤波算法的路面构造深度计算方法[J].东南大学学报(自然科学版),2020,50(1):129-136.[doi:10.3969/j.issn.1001-0505.2020.01.017]
 Lou Yuexin,Chen Shengdi,Lu Jian,et al.Calculation method for pavement macrotexture depth based on improved Kalman filter algorithm[J].Journal of Southeast University (Natural Science Edition),2020,50(1):129-136.[doi:10.3969/j.issn.1001-0505.2020.01.017]
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基于改进卡尔曼滤波算法的路面构造深度计算方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
50
期数:
2020年第1期
页码:
129-136
栏目:
交通运输工程
出版日期:
2020-01-13

文章信息/Info

Title:
Calculation method for pavement macrotexture depth based on improved Kalman filter algorithm
作者:
娄月新1陈圣迪2陆键1郎洪1
1同济大学道路与交通工程教育部重点实验室, 上海 201804; 2上海海事大学交通运输学院, 上海 201306
Author(s):
Lou Yuexin1 Chen Shengdi2 Lu Jian1 Lang Hong1
1The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
2College of Transportation Engineering, Shanghai Maritime University, Shanghai 201306, China
关键词:
路面构造深度 卡尔曼滤波 滑动滤波 铺砂法 平均剖面深度
Keywords:
pavement macrotexture depth Kalman filter sliding filter sand patch method mean profile depth
分类号:
U416.2
DOI:
10.3969/j.issn.1001-0505.2020.01.017
摘要:
为了提高路面构造深度的测量精度,提出了基于改进卡尔曼滤波算法的路面构造深度计算方法.首先,利用统计检验法对高精度激光距离传感器获取的路面高程值进行异常值筛选及插值修正.其次,基于改进卡尔曼滤波算法对修正后的数据进行滤波,并建立平均剖面深度模型来计算路面剖面深度值.随后选取AC-13沥青混凝土和SMA-13沥青混凝土2种路面作为试验样本,对改进卡尔曼滤波算法与滑动滤波算法和铺砂法进行了对比验证,并建立了2种路面类型的构造深度转换模型.研究结果表明:对于AC-13和SMA-13两种路面类型,改进卡尔曼滤波算法的均方误差分别为0.001 3和0.002 0,平均绝对百分比误差分别为2.92%和3.85%,与铺砂法的相关系数大于0.95,重复性标准偏差及变异系数均小于5%.所提方法具有更高的测量精度和良好的稳定性,能够准确地测量计算路面构造深度.
Abstract:
To improve the measurement accuracy of pavement macrotexture depth, an improved Kalman filter algorithm was proposed to calculate pavement macrotexture depth. First, the outliers of road elevation values obtained by a high precision laser range sensor were screened and interpolated by a statistical test method. Secondly, the corrected data were filtered by an improved Kalman filter algorithm, and the mean profile depth model was established to calculate the pavement profile depth. Then, the AC-13 asphalt concrete pavement and the SMA-13 asphalt concrete pavement were selected as test samples to compare the improved Kalman filter algorithm with the sliding filter algorithm and the sand patch method, and the macrotexture depth conversion models of the two pavement types were established. The results show that for the two pavement types of AC-13 and SMA-13, the mean square errors of the improved Kalman filter algorithm are 0.001 3 and 0.002 0 and the average absolute percentage errors are 2.92% and 3.85%. The correlation index with the sand patch method is more than 0.95, and both the repeatability standard deviation and the variation coefficient are less than 5%. The method has higher measurement accuracy and better stability. Thus, it can accurately measure and calculate pavement macrotexture depth.

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备注/Memo

备注/Memo:
收稿日期: 2019-05-21.
作者简介: 娄月新(1993—),男,博士生;陈圣迪(联系人),女,博士,讲师,sdchen@shmtu.edu.cn.
基金项目: 国家重点研发计划资助项目(2017YFC0803902).
引用本文: 娄月新,陈圣迪,陆键,等.基于改进卡尔曼滤波算法的路面构造深度计算方法[J].东南大学学报(自然科学版),2020,50(1):129-136. DOI:10.3969/j.issn.1001-0505.2020.01.017.
更新日期/Last Update: 2020-01-20