摘要
针对目前检测算法无法准确检测电力铁塔塔身的主材形变问题,提出了基于载波相位差分的电力铁塔塔身主材形变检测算法。该算法首先结合局部均值分解方法和支持向量回归机检测并修复卫星信号的周跳;其次,建立载波相位差分检测模型,在形变检测过程中,通过卡尔曼滤波对检测模型进行更新;最后,采用LAMBDA算法对载波相位差分检测模型中的整周模糊度展开计算,并将计算结果代入模型中,利用更新后的载波相位差分检测模型实现电力铁塔塔身主材形变的检测。实验结果表明:本文算法的周跳检测精度高、修复效果好、形变检测精度高。
At present,the detection algorithm cannot accurately detect the deformation of the main material of the power tower body.Therefore,a carrier phase difference based deformation detection algorithm for the main material of the power tower body was proposed.Firstly,combining local mean decomposition method and support vector regression machine to detect and repair cycle jumps in satellite signals.Secondly,a carrier phase differential detection model was established,and during the deformation detection process,the detection model was updated through Kalman filtering.Finally,the LAMBDA algorithm was used to calculate the integer ambiguity in the carrier phase differential detection model,and the calculation results are substituted into the model.The updated carrier phase differential detection model is used to detect the deformation of the main material of the power tower body.The experimental results show that the proposed algorithm has high cycle slip detection accuracy,good repair effect,and high deformation detection accuracy.
作者
刘义艳
代杰
LIU Yi-yan;DAI Jie(School of Energy and Electrical Engineering,Chang'an University,Xi'an 710064,China)
出处
《吉林大学学报(工学版)》
CSCD
北大核心
2024年第12期3693-3698,共6页
Journal of Jilin University:Engineering and Technology Edition
基金
陕西省重点研发计划项目(2021GY-098)
国家重点研发计划项目(2021YFB2601300)。
关键词
载波相位差分
局部均值分解
支持向量回归机
LAMBDA算法
形变检测
carrier phase difference
local mean decomposition
support vector regression machine
LAMBDA algorithm
deformation detection
