Next Article in Journal
A Robust and Device-Free System for the Recognition and Classification of Elderly Activities
Previous Article in Journal
Femtosecond Laser Ablated FBG with Composite Microstructure for Hydrogen Sensor Application
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(12), 2036; doi:10.3390/s16122036

Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
3
Research Center of Remote Sensing in Public Security, People’s Public Security University of China, Beijing 100038, China
4
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
5
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Mehrez Zribi
Received: 19 September 2016 / Revised: 14 November 2016 / Accepted: 24 November 2016 / Published: 1 December 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [5286 KB, uploaded 1 December 2016]   |  

Abstract

Low-cost GPS (receiver) has become a ubiquitous and integral part of our daily life. Despite noticeable advantages such as being cheap, small, light, and easy to use, its limited positioning accuracy devalues and hampers its wide applications for reliable mapping and analysis. Two conventional techniques to remove outliers in a GPS trajectory are thresholding and Kalman-based methods, which are difficult in selecting appropriate thresholds and modeling the trajectories. Moreover, they are insensitive to medium and small outliers, especially for low-sample-rate trajectories. This paper proposes a model-based GPS trajectory cleaner. Rather than examining speed and acceleration or assuming a pre-determined trajectory model, we first use cubic smooth spline to adaptively model the trend of the trajectory. The residuals, i.e., the differences between the trend and GPS measurements, are then further modeled by time series method. Outliers are detected by scoring the residuals at every GPS trajectory point. Comparing to the conventional procedures, the trend-residual dual modeling approach has the following features: (a) it is able to model trajectories and detect outliers adaptively; (b) only one critical value for outlier scores needs to be set; (c) it is able to robustly detect unapparent outliers; and (d) it is effective in cleaning outliers for GPS trajectories with low sample rates. Tests are carried out on three real-world GPS trajectories datasets. The evaluation demonstrates an average of 9.27 times better performance in outlier detection for GPS trajectories than thresholding and Kalman-based techniques. View Full-Text
Keywords: GPS trajectory; outlier detection; cubic smooth spline; time series; estimation GPS trajectory; outlier detection; cubic smooth spline; time series; estimation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Chen, X.; Cui, T.; Fu, J.; Peng, J.; Shan, J. Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories. Sensors 2016, 16, 2036.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top