@article{LIU2007, 
author = {Dan LIU and Jinwei SUN and Guo WEI},
title = {Algorithmic Study of M-Estimators for Multi-Function Sensor Data Reconstruction},
year = {2007},
journal = {Tsinghua Science and Technology},
volume = {12},
number = {1},
pages = {9-13},
keywords = {least squares, weighted least squares, M-estimators, data reconstruction},
url = {https://www.sciopen.com/article/10.1016/S1007-0214(07)70002-0},
doi = {10.1016/S1007-0214(07)70002-0},
abstract = {This paper describes a data reconstruction technique for a multi-function sensor based on the M-estimator, which uses least squares and weighted least squares method. The algorithm has better robustness than conventional least squares which can amplify the errors of inaccurate data. The M-estimator places particular emphasis on reducing the effects of large data errors, which are further overcome by an iterative regression process which gives small weights to large off-group data errors and large weights to small data errors. Simulation results are consistent with the hypothesis with 81 groups of regression data having an average accuracy of 3.5%, which demonstrates that the M-estimator provides more accurate and reliable data reconstruction.}
}