DTM-aided adaptive EPF navigation application in railways

Jin, C, Cai, B, Wang, J and Kealy, A 2018, 'DTM-aided adaptive EPF navigation application in railways', Sensors, vol. 18, no. 11, pp. 1-16.


Document type: Journal Article
Collection: Journal Articles

Title DTM-aided adaptive EPF navigation application in railways
Author(s) Jin, C
Cai, B
Wang, J
Kealy, A
Year 2018
Journal name Sensors
Volume number 18
Issue number 11
Start page 1
End page 16
Total pages 16
Publisher MDPIAG
Abstract The diverse operating environments change GNSS measurement noise covariance in real time, and different GNSS techniques hold different measurement noise covariance as well. Mismodelling the covariance causes undependable filtering results and even degenerates the GNSS/INS Particle Filter (PF) process, due to the fact that INS error-state noise covariance is much smaller than that of GNSS measurement noise. It also makes the majority of existing methods for adaptively adjusting filter parameters incapable of performing well. In this paper, a feasible Digital Track Map-aided (DTM-aided) adaptive extended Kalman particle filter method is introduced in GNSS/INS integration in order to adjust GNSS measurement noise covariance in real time, and the GNSS down-direction offset is also estimated along with every sampling period through making full use of DTM information. The proposed approach is successfully examined in a railway environment, and the on-site experimental results reveal that the adaptive approach holds better positioning performance in comparison to the methods without adaptive adjustment. Improvements of 62.4% and 14.9% in positioning accuracy are obtained in contrast to Standard Point Positioning (SPP) and Precise Point Positioning (PPP), respectively. The proposed adaptive method takes advantage of DTM information and is able to automatically adapt to complex railway environments and different GNSS techniques.
Subject Analytical Chemistry not elsewhere classified
Keyword(s) Adaptive filtering
Digital track map
Extended kalman particle filter
Train navigation application
DOI - identifier 10.3390/s18113860
Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
ISSN 1424-8220
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