Abstract If there are no ideas of the locations of gross errors, selecting weight iteration method runs the first adjustment with equal weight. It leads to gross errors transferred to other observations. The robust estimation will fail because of incorrect weight. The first order minimum norm has the ability to resist gross errors, but is not optimal under Gaussian distribution. Combined two methods above, which the first order minimum norm is used to determine the location of gross errors, and then selecting weight iteration method reduces weight of those gross errors, we propose a novel robust estimation. The novel method is validated by a level net data processing.
ZHAO Yan,LI Muhan,WANG Peng et al. A Robust Estimation Method Combined First Order Minimum Norm and Selecting Weight Iteration Method
[J]. jgg, 2016, 36(4): 331-333.
ZHAO Yan,LI Muhan,WANG Peng et al. A Robust Estimation Method Combined First Order Minimum Norm and Selecting Weight Iteration Method
[J]. jgg, 2016, 36(4): 331-333.
