A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF

In the traditional MEMS-INS/GNSS integration, velocity and position are regarded as measurement information. However, it will lead to unobservable of the heading angle, and the heading angle error will be divergent. Eventually, the navigation accuracy will be reduced either. In this paper, a novel a...

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Main Authors: Ping Dong, Jianhua Cheng, Liqiang Liu, Xiangyu Sun, Shilong Fan
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8876845/
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spelling doaj-65c20481b09649079e784cc8aad59db92021-03-30T00:51:43ZengIEEEIEEE Access2169-35362019-01-01715408415409510.1109/ACCESS.2019.29483688876845A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKFPing Dong0https://orcid.org/0000-0001-6333-2676Jianhua Cheng1Liqiang Liu2Xiangyu Sun3Shilong Fan4College of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaIn the traditional MEMS-INS/GNSS integration, velocity and position are regarded as measurement information. However, it will lead to unobservable of the heading angle, and the heading angle error will be divergent. Eventually, the navigation accuracy will be reduced either. In this paper, a novel approach of heading angle estimation based on Zero-Heading angle-Variation-Constraint (ZHVC) and Sequential-Adaptive Unscented Kalman Filter (SAUKF) algorithms is proposed for avoiding the heading angle unobservability and unstable of the filtering. First, we inspired by the vehicle dynamics and add new information to the original measurement vectors. This new measurement information is the difference of heading angle variations from the MEMS-INS and the theoretical value. Second, we separate the measurement update of Unscented Kalman Filter (UKF) to two parts by sequential method. One is the measurement information of velocity and position, the other one is the heading angle variation. Meanwhile, adaptive UKF only estimate the heading angle variation measurement noise covariance matrix in real-time. The simulation and experiment show that ZHVC can improve the observable degree and accuracy of heading angle than the common method. The SAUKF can estimate heading angle variation measurement noise covariance matrix in real-time. And the filter results are more stable in different motions of the vehicle.https://ieeexplore.ieee.org/document/8876845/ZHVCSAUKFobservable degreemeasurement noise covariance matrix
collection DOAJ
language English
format Article
sources DOAJ
author Ping Dong
Jianhua Cheng
Liqiang Liu
Xiangyu Sun
Shilong Fan
spellingShingle Ping Dong
Jianhua Cheng
Liqiang Liu
Xiangyu Sun
Shilong Fan
A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF
IEEE Access
ZHVC
SAUKF
observable degree
measurement noise covariance matrix
author_facet Ping Dong
Jianhua Cheng
Liqiang Liu
Xiangyu Sun
Shilong Fan
author_sort Ping Dong
title A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF
title_short A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF
title_full A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF
title_fullStr A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF
title_full_unstemmed A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF
title_sort heading angle estimation approach for mems-ins/gnss integration based on zhvc and saukf
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description In the traditional MEMS-INS/GNSS integration, velocity and position are regarded as measurement information. However, it will lead to unobservable of the heading angle, and the heading angle error will be divergent. Eventually, the navigation accuracy will be reduced either. In this paper, a novel approach of heading angle estimation based on Zero-Heading angle-Variation-Constraint (ZHVC) and Sequential-Adaptive Unscented Kalman Filter (SAUKF) algorithms is proposed for avoiding the heading angle unobservability and unstable of the filtering. First, we inspired by the vehicle dynamics and add new information to the original measurement vectors. This new measurement information is the difference of heading angle variations from the MEMS-INS and the theoretical value. Second, we separate the measurement update of Unscented Kalman Filter (UKF) to two parts by sequential method. One is the measurement information of velocity and position, the other one is the heading angle variation. Meanwhile, adaptive UKF only estimate the heading angle variation measurement noise covariance matrix in real-time. The simulation and experiment show that ZHVC can improve the observable degree and accuracy of heading angle than the common method. The SAUKF can estimate heading angle variation measurement noise covariance matrix in real-time. And the filter results are more stable in different motions of the vehicle.
topic ZHVC
SAUKF
observable degree
measurement noise covariance matrix
url https://ieeexplore.ieee.org/document/8876845/
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