Robust Estimation Peculiarity of Robust Estimation Methods in Observations to Obey Generalized Gaussian Distribution

Peng Fei Xing; Yong Hui Ge; Yan Li

doi:10.4028/www.scientific.net/AMM.556-562.4380

Paper Titles

Optimization of the Distribution Geometry of Station by PSO Based on the System of Velocity Measurement and Orbit Determining
p.4361

An Efficient Hardware-Oriented Algorithm of Spatial Motion Vector Prediction for AVS HD Video Encoder
p.4365

Improved Unscented Kalman Filter Based on Decimation in Frequency Domain Fast Fourier Transform for Attitude Estimation Applied to Underwater Gliders
p.4372

CRFs Based Chinese Word Segmentation
p.4376

Robust Estimation Peculiarity of Robust Estimation Methods in Observations to Obey Generalized Gaussian Distribution
p.4380

Stabilization of Fuzzy Markovian Jumping Systems with Distributed Delay
p.4386

Parameters Optimization of Restraint System by Analyzing the Dissipation Characteristics of Occupant Energy in Vehicle Frontal Impact
p.4391

Research on Ontology Structure Characteristics Analysis and Matching Application
p.4396

Pressure Sensing Characteristic of Fiber Bragg Grating Based on Diaphragm-Cantilever Beam
p.4400

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Robust Estimation Peculiarity of Robust Estimation...

Robust Estimation Peculiarity of Robust Estimation Methods in Observations to Obey Generalized Gaussian Distribution

Abstract:

Robust estimation method in generalized Gaussian distribution of observations under obedience can effectively eliminate or reduce the influence of gross errors, however, peculiarity of different estimation methods are not the same. In this paper, it’s used simulation method, the commonly used 13 kinds of robust features robust estimation methods were compared. The results showed that: L1 method, Danish method, German-McClure method and IGGIII program is more efficient robust estimation methods in Observations to obey generalized gaussian distribution, which method is more effective than other commonly used to eliminate the impact of robust estimation of gross errors or weaken .

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

4380-4385

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.4380

Citation:

Cite this paper

Online since:

May 2014

Authors:

Peng Fei Xing*, Yong Hui Ge, Yan Li

Keywords:

Gross Errors, Robust Estimation Methods

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Jiangwen Zhou. Classical theory and robust estimation[J]. Journal of Surveying and Mapping, 1989, 18 (2).

Google Scholar

[2] Youcai Huang. Robust estimation theory summary Summary. Metallurgical Mapping, 1993，1(6-9).

Google Scholar

[3] Taiyue Wang, Zhiming Li. A generalized Gaussian distribution parameter estimation method quickly. (2006).

Google Scholar

[4] Jianhua Hou, Chengyi Xiong, Xiaomei Tian. Generalized Gaussian Distribution and Its Application in Image Denoising[J]. Journal of Central South University: Natural Science Edition, 2005, 24(3): 44-47.

Google Scholar

[5] M N Do, M Vetterli. Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance[J]. IEEE Trans. Image Process, 2002, 11: 146-158.

DOI: 10.1109/83.982822

Google Scholar

[6] Jia N. N and Ge Y. H. Remainder reliability and robust estimation: A case study using twelve simulated leveling networks [C]/ Zhang J. Information Technology Applications in Industry. Switzerland: Trans Tech Publications, 2013: 3163-3167.

DOI: 10.4028/www.scientific.net/amm.263-266.3163

Google Scholar