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Sensors 2017, 17(9), 2032; https://doi.org/10.3390/s17092032

An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation

1
,
1,* , 1,2,* and 3
1
School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
2
Geomatics Engineering Department, University of Calgary, Calgary, AB T2N 1N4, Canada
3
Space Star Technology Co. Ltd., CAST, Haidian District, Beijing 100086, China
*
Authors to whom correspondence should be addressed.
Received: 20 July 2017 / Revised: 3 September 2017 / Accepted: 4 September 2017 / Published: 5 September 2017
(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)

Abstract

The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes. View Full-Text
Keywords: tightly coupled navigation; measurement noise covariance estimation; adaptive Kalman filter (AKF); unscented Kalman filter (UKF); satellite selection tightly coupled navigation; measurement noise covariance estimation; adaptive Kalman filter (AKF); unscented Kalman filter (UKF); satellite selection
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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).
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Li, Z.; Zhang, H.; Zhou, Q.; Che, H. An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation. Sensors 2017, 17, 2032.

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