Next Article in Journal
How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy
Previous Article in Journal
Active Low Intrusion Hybrid Monitor for Wireless Sensor Networks
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(9), 23953-23982;

An Adaptive Low-Cost GNSS/MEMS-IMU Tightly-Coupled Integration System with Aiding Measurement in a GNSS Signal-Challenged Environment

1,2,* , 1
Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
University of Calgary, 2500 University Drive N.W. Calgary, AL T2N1N4, Canada
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 9 July 2015 / Revised: 10 September 2015 / Accepted: 14 September 2015 / Published: 18 September 2015
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [1058 KB, uploaded 18 September 2015]   |  


The main aim of this paper is to develop a low-cost GNSS/MEMS-IMU tightly-coupled integration system with aiding information that can provide reliable position solutions when the GNSS signal is challenged such that less than four satellites are visible in a harsh environment. To achieve this goal, we introduce an adaptive tightly-coupled integration system with height and heading aiding (ATCA). This approach adopts a novel redundant measurement noise estimation method for an adaptive Kalman filter application and also augments external measurements in the filter to aid the position solutions, as well as uses different filters to deal with various situations. On the one hand, the adaptive Kalman filter makes use of the redundant measurement system’s difference sequence to estimate and tune noise variance instead of employing a traditional innovation sequence to avoid coupling with the state vector error. On the other hand, this method uses the external height and heading angle as auxiliary references and establishes a model for the measurement equation in the filter. In the meantime, it also changes the effective filter online based on the number of tracked satellites. These measures have increasingly enhanced the position constraints and the system observability, improved the computational efficiency and have led to a good result. Both simulated and practical experiments have been carried out, and the results demonstrate that the proposed method is effective at limiting the system errors when there are less than four visible satellites, providing a satisfactory navigation solution. View Full-Text
Keywords: tightly-coupled integration; adaptive Kalman filter; GPS; MEMS-IMU tightly-coupled integration; adaptive Kalman filter; GPS; MEMS-IMU

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).

Share & Cite This Article

MDPI and ACS Style

Zhou, Q.; Zhang, H.; Li, Y.; Li, Z. An Adaptive Low-Cost GNSS/MEMS-IMU Tightly-Coupled Integration System with Aiding Measurement in a GNSS Signal-Challenged Environment. Sensors 2015, 15, 23953-23982.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



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