Detection of Outliers in GPS Measurements by Using Functional-Data Analysis

The identification of outliers in global positioning system (GPS) observations—to compare equipment, positioning methods, or working conditions—has traditionally been performed using univariate or multivariate statistics. However, these methods have certain drawbacks when processing data collected by GPS receivers. Such data can be more suitably handled as observations at discrete points of a smooth stochastic process and, consequently, other statistical approaches to the analysis of functional data may prove more suitable. We analyzed the applicability of the concept of functional depth to the identification of outliers in GPS observations. The proposed method was applied to 12 series of GPS receiver data collected in an open space and in similar signal reception conditions. The results obtained adapted better to the expected results, given the signal-reception conditions, than those obtained by the classical statistical approaches used by other writers to compare GPS observations.

• Record URL:
  https://doi.org/10.1061/(ASCE)SU.1943-5428.0000056
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/07339453
• Supplemental Notes:
  • Copyright © 2011 American Society of Civil Engineers
• Authors:
  • Ordonez, C
  • Martinez, J
  • Rodriguez-Perez, J. R.
  • Reyes, A
• Publication Date: 2011-11-1

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 150-155
• Serial:
  • Journal of Surveying Engineering
  • Volume: 137
  • Issue Number: 4
  • Publisher: American Society of Civil Engineers
  • ISSN: 0733-9453
  • Serial URL: http://ascelibrary.org/journal/jsued2

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Data quality; Global Positioning System; Measurement; Navigation systems; Real time data processing; Real time information; Reliability; Satellite navigation systems; Signal processing; Statistical analysis; Surveys
• Uncontrolled Terms: Outliers
• Subject Areas: Data and Information Technology; Geotechnology; Highways; I70: Traffic and Transport;

Filing Info

• Accession Number: 01363254
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: Feb 22 2012 9:37AM