测绘学报 ›› 2018, Vol. 47 ›› Issue (9): 1171-1178.doi: 10.11947/j.AGCS.2018.20170344

• 大地测量学与导航 • 上一篇    下一篇

系数矩阵中部分有界不确定性的混合平差算法

王志忠1, 宋迎春2, 何玲莉1   

  1. 1. 中南大学数学与统计学院, 湖南 长沙 410083;
    2. 中南大学地球信息科学与物理学院, 湖南 长沙 410083
  • 收稿日期:2017-06-21 修回日期:2018-03-21 出版日期:2018-09-20 发布日期:2018-09-26
  • 通讯作者: 何玲莉 E-mail:helingli@csu.edu.cn
  • 作者简介:王志忠(1963-),男,博士,博士生导师,研究方向为测量数据处理。E-mail:wzz8713761@163.com
  • 基金资助:
    国家自然科学基金(41574006;41674009;41674012);中南大学研究生自主探索创新项目(1053320170182)

Mixed Adjustment Algorithm for Part of the Coefficient Matrix with Uncertainty

WANG Zhizhong1, SONG Yingchun2, HE Lingli1   

  1. 1. School of Mathematics and Statistics, Central South University, Changsha 410083, China;
    2. School of Geosciences and Info-physic, Central South University, Changsha 410083, China
  • Received:2017-06-21 Revised:2018-03-21 Online:2018-09-20 Published:2018-09-26
  • Supported by:
    The National Natural Science Foundation of China (Nos. 41574006;41674009;41674012);Independent Exploration and Innovation Project of Graduate students of Central South University (No. 1053320170182)

摘要: 在测量数据的获取过程中,常常存在着不确定性。它们影响着参数估计的有效性和可靠性。本文基于不确定性混合平差模型,在不确定性误差有界的约束下,利用随机误差和不确定性误差平方和达最小的新平差准则,给出了一个新的不确定性平差模型迭代算法。通过算例,对本文算法与其他方法进行了比较。结果表明:本文所提参数解算方法是有效可行的,且在不确定性较大时,该方法有较好的适用性。

关键词: 混合平差模型, 平差准则, 不确定性, 最小二乘估计

Abstract: Uncertainty often exists in the process of measurement data acquiring,which affects the reliability and validity of parameter estimation.Based on uncertain mixed adjustment model,this paper applies the adjustment criterion,minimizing the sum of squares of random error and squares of uncertainty error,to study a new iteration algorithm to solve the adjustment model under the bound constrain of uncertainty.By the example,the estimation results of proposed method are compared with that of another relative method.The results show that the parameter calculation method presented in this paper is effective and feasible.Meanwhile,the method has satisfied applicability when the uncertainty is large.

Key words: mixed adjustment model, adjustment criterion, uncertainty, least-squares estimation

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