Next Article in Journal
A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems
Previous Article in Journal
Realization of the Zone Length Measurement during Zone Refining Process via Implementation of an Infrared Camera
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle

GNSS Positioning Accuracy Enhancement Based on Robust Statistical MM Estimation Theory for Ground Vehicles in Challenging Environments

Brain-inspired Application Technology Center (BATC), School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(6), 876; https://doi.org/10.3390/app8060876
Received: 3 April 2018 / Revised: 11 May 2018 / Accepted: 14 May 2018 / Published: 25 May 2018
  |  
PDF [6794 KB, uploaded 25 May 2018]
  |  

Abstract

Global Navigation Satellite System (GNSS) is the most reliable navigation system for location-based applications where accuracy and consistency is an essential requirement. The LSE (least squares estimator) has been used since the start of GNSS for position estimation. However; LSE is affected by outliers and errors in GNSS measurements and results in wrong user position. In this paper; we proposed a novel three-phase estimator for enhancing GNSS positioning accuracy in the presence of outliers and errors; relying upon the robust MM estimation theory. In the first phase; a subsampling process is proposed on available observations. IRWLS (iterative reweighted LS) is applied to all subsamples up to a predefined number of observations to obtain a positioning estimate and a scale factor. Secondly; IRWLS is applied up to the convergence point on a set of selected subsamples. The third phase involves the selection of optimum positioning solution having minimum scale factor. An outlier detection and exclusion process is applied on a probabilistic set of outlying observations to maintain the integrity and reliability of the position. Multiple simulated and real scenarios are tested. Results show high accuracy and reliability of the proposed algorithm in challenging environments. View Full-Text
Keywords: multi-GNSS; GNSS navigation; LSE; IRWLS; urban canyon; outliers; MM estimation multi-GNSS; GNSS navigation; LSE; IRWLS; urban canyon; outliers; MM 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

Share & Cite This Article

MDPI and ACS Style

Akram, M.A.; Liu, P.; Wang, Y.; Qian, J. GNSS Positioning Accuracy Enhancement Based on Robust Statistical MM Estimation Theory for Ground Vehicles in Challenging Environments. Appl. Sci. 2018, 8, 876.

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]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top