王乐洋, 邹传义. PEIV模型参数估计理论及其应用研究进展[J]. 武汉大学学报 ( 信息科学版), 2021, 46(9): 1273-1283, 1297. DOI: 10.13203/j.whugis20200312
引用本文: 王乐洋, 邹传义. PEIV模型参数估计理论及其应用研究进展[J]. 武汉大学学报 ( 信息科学版), 2021, 46(9): 1273-1283, 1297. DOI: 10.13203/j.whugis20200312
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WANG Le-yang, ZOU Chuan-yi. Review of PEIV Model Parameter Estimation Theory and Its Applications[J]. Geomatics and Information Science of Wuhan University, 2021, 46(9): 1273-1283, 1297. DOI: 10.13203/j.whugis20200312
Citation: WANG Le-yang, ZOU Chuan-yi. Review of PEIV Model Parameter Estimation Theory and Its Applications[J]. Geomatics and Information Science of Wuhan University, 2021, 46(9): 1273-1283, 1297. DOI: 10.13203/j.whugis20200312
shu

PEIV模型参数估计理论及其应用研究进展

Review of PEIV Model Parameter Estimation Theory and Its Applications

    摘要
  • 摘要: 部分变量含误差(partial errors-in-variables, PEIV)模型是变量含误差(errors-in-variables, EIV)模型的更一般形式, 因其适用性强等优势, 近十年来被广泛应用于全球导航卫星系统(global navigation satellite system, GNSS)数据处理、坐标转换、变形监测和数据拟合等测量数据处理中。概述了PEIV模型的发展过程, 从PEIV模型参数估计算法、精度评定、随机模型估计、扩展算法和数据处理应用5个核心问题进行综述和分析。对PEIV模型的应用进行了展望, 指出有待进一步研究的问题, 旨在进一步推动测绘数据处理的发展, 并为读者提供参考和建议。

     

    Abstract:
      Objectives  Partial errors-in-variables(PEIV) model is a more general form of errors in variables(EIV) model. It has been widely used in the data processing of global navigation satellite system(GNSS), coordinate conversion, deformation monitoring and data fitting in the past ten years.
      Methods  First, the development history of PEIV model is reviewed. Then, five core problems of PEIV model are summarized and analyzed, including parameter estimation algorithm, precision estimation, random model estimation, expansion algorithm and data processing application. Finally, the applications of PEIV model are outlined.
      Results  We point out three questions for further research, including how to process big data by PEIV parameter estimation method, how to correct the bias of weighted total least squares(WTLS) adjustment by sampling method, and how to derive the formula of higher order parameter estimation covariance matrix are.
      Conclusions  This review paper aims to further promote the development of surveying and mapping data processing and provide readers with suggestions and references.

     

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