Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81099
Title: Research on time-correlated errors using allan variance in a Kalman filter applicable to vector-tracking-based GNSS software-defined receiver for autonomous ground vehicle navigation
Authors: Luo, YR
Li, J
Yu, CY
Xu, B 
Li, Y
Hsu, LT 
El-Sheimy, N
Keywords: Global navigation satellite system (GNSS)
Software-defined receiver (SDR)
Vector tracking (VT)
Kalman filter (KF)
Allan variance
Time-correlated error
Gauss-Markov (GM) process
Innovation sequence
RTKLIB
Issue Date: 2019
Publisher: Molecular Diversity Preservation International
Source: Remote sensing, 1 May 2019, v. 11, no. 9, 1026, p. 1-39 How to cite?
Journal: Remote sensing 
Abstract: The global navigation satellite system (GNSS) has been applied to many areas, e.g., the autonomous ground vehicle, unmanned aerial vehicle (UAV), precision agriculture, smart city, and the GNSS-reflectometry (GNSS-R), being of considerable significance over the past few decades. Unfortunately, the GNSS signal performance has the high risk of being reduced by the environmental interference. The vector tracking (VT) technique is promising to enhance the robustness in high dynamics as well as improve the sensitivity against the weak environment of the GNSS receiver. However, the time-correlated error coupled in the receiver clock estimations in terms of the VT loop can decrease the accuracy of the navigation solution. There are few works present dealing with this issue. In this work, the Allan variance is accordingly exploited to specify a model which is expected to account for this type of error based on the 1st-order Gauss-Markov (GM) process. Then, it is used for proposing an enhanced Kalman filter (KF) by which this error can be suppressed. Furthermore, the proposed system model makes use of the innovation sequence so that the process covariance matrix can be adaptively adjusted and updated. The field tests demonstrate the performance of the proposed adaptive vector-tracking time-correlated error suppressed Kalman filter (A-VTTCES-KF). When compared with the results produced by the ordinary adaptive KF algorithm in terms of the VT loop, the real-time kinematic (RTK) positioning and code-based differential global positioning system (DGPS) positioning accuracies have been improved by 14.17% and 9.73%, respectively. On the other hand, the RTK positioning performance has been increased by maximum 21.40% when compared with the results obtained from the commercial low-cost U-Blox receiver.
URI: http://hdl.handle.net/10397/81099
EISSN: 2072-4292
DOI: 10.3390/rs11091026
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Luo, Y.; Li, J.; Yu, C.; Xu, B.; Li, Y.; Hsu, L.-T.; El-Sheimy, N. Research on Time-Correlated Errors Using Allan Variance in a Kalman Filter Applicable to Vector-Tracking-Based GNSS Software-Defined Receiver for Autonomous Ground Vehicle Navigation. Remote Sens. 2019, 11, 1026, 39 pages is available at https://dx.doi.org/10.3390/rs11091026
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