摘要
针对大型桥梁桥塔与基站高程差异较大,残余对流层延迟成为影响全球卫星导航系统(GNSS)监测成功率与精度的主要因素之一。该文基于随机过程理论,对桥梁监测GNSS残余对流层湿延迟进行参数估计,有效地提高了桥梁塔顶监测GNSS模糊度固定率。通过采用对流层经验模型改正对流层干延迟,将基准站和塔顶观测站对流层湿延迟组成相对对流层湿延迟,并联合位置参数和模糊度参数建立双差卡尔曼模型,最后利用最小二乘模糊度降低相关平差法(LAMBDA)对双差模糊度进行固定,并估计位置参数与相对对流层延迟参数。实验结果表明,该方法可以有效估计相对对流层延迟,有效提高GNSS模糊度固定率。
In view of the large difference in elevation between large bridge towers and base stations,residual tropospheric delay has become one of the main factors affecting the success rate and accuracy of global navigation satellite system(GNSS)monitoring.Based on the random process theory,the parameters of GNSS residual tropospheric wet delay was estimated for bridge monitoring,and the fixed rate of GNSS ambiguity was improved effectively for bridge tower monitoring in this paper.By using the tropospheric empirical model to correct the tropospheric dry delay,relative tropospheric wet delay was composed of the tropospheric wet delay of the base station and the tower top station,and the double difference Kalman model were established by combining the position parameter and the ambiguity parameter,and finally the least squares ambiguity were reduced.The least-squares ambiguity decorrelation adjustment method(LAMBDA)was used to fix the double-difference ambiguity and estimate the position parameters and relative tropospheric delay parameters.The experimental results showed that the proposed method could effectively estimate the relative tropospheric delay and effectively improve the GNSS ambiguity fixed rate.
作者
高兴旺
李付岗
张秋昭
吴来义
杨威
戴新军
GAO Xing wang;LI Fugang;ZHANG Qiuzhao;WU Laiyi;YANG Wei;DAI Xinjun(Jiangsu Key Laboratory of Resources and Environmental Information Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China;Jizhong Energy Fengfeng Group Sunzhuang Mining Co.,L td.,Handan,Hebei 056200,China;China Railway Bridge(Nanjing)Bridge and Tunnel Diagnosis and Treatment Co.,Ltd.,Nanjing 210061,China;Guangzhou Haida Ankong Intelligent Technology Co.,Ltd.,Guangzhou 511400,China)
出处
《测绘科学》
CSCD
北大核心
2021年第2期42-47,70,共7页
Science of Surveying and Mapping
基金
江苏省政府政策引导计划国际科技合作项目(BZ2017056)
江苏省资源环境信息工程重点实验室开放基金项目(JS201904)
国家自然科学基金资助项目(41811530304)。