A New Algorithm of Weighted Total Least Squares Estimate of Partial EIV Model Based on an Improved Objective Function

ZHAO Jun; GUI Qingming; GUO Feixiao

doi:10.13203/j.whugis20150180

Volume 42 Issue 8

Aug. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(8): 1179-1184. > DOI: 10.13203/j.whugis20150180

ZHAO Jun, GUI Qingming, GUO Feixiao. A New Algorithm of Weighted Total Least Squares Estimate of Partial EIV Model Based on an Improved Objective Function[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8): 1179-1184. DOI: 10.13203/j.whugis20150180

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A New Algorithm of Weighted Total Least Squares Estimate of Partial EIV Model Based on an Improved Objective Function

ZHAO Jun^{1, 2, 3,},
GUI Qingming³,
GUO Feixiao^{2, 4}

1.
Xi'an Technical Division of Surveying and Mapping, Xi'an 710054, China
2.
State Key Laboratory of Geodesy and Earth's Dynamics, Wuhan 430077, China
3.
Institute of Geographical Spatial Information, Information Engineering University, Zhengzhou 450001, China
4.
Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China

Funds:

The National Natural Science Foundation of China 41174005

The National Natural Science Foundation of China 41474009

State Key Laboratory of Geodesy and Earth′s Dynamics SKLGED 2017-3-2-E

State Key Laboratory of Geo-information Engineering SKLGIE2015-M-1-2

State Key Laboratory of Geo-information Engineering SKLGIE2015-M-3-2

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Author Bio:
ZHAO Jun, PhD, specializes in surveying data processing. E-mail: Zhaojun4368@163.com
Received Date: March 04, 2016
Published Date: August 04, 2017

Graphical Abstract

Abstract

Abstract

The computation of weighted total least squares (WTLS) estimate of partial EIV model requires more iterations and more computation burden. Therefore, the study has proposed a new algorithm for computing the WTLS estimate of partial EIV model by improving objective function based on the weighted LS principle and applying the differential and inversion transformation of matrix. The results of numerical examples show that the new algorithm requires less iterations and more superior in the sense of computational efficiency.
- partial EIV model,
- weighted total least-squares,
- improved objective function,
- errors-in-variables

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References

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