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

Dong, Peng; Cheng, Jianhua; Liu, Liqiang; Sun, Xianwen; Fan, Shilong

doi:10.1109/access.2019.2948368

Cited by 3 publications

(5 citation statements)

References 20 publications

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“…Then we can get ( 15) from ( 2) and ( 14). After that a series of simplification is conducted by elementary rank transformations of matrix in (16). It should be noted that the rank of the system can reflect the observability of the system in some degree.…”

Section: Observability Analysis Of Gnss/ins Integration Systemmentioning

confidence: 99%

“…It should be noted that the rank of the system can reflect the observability of the system in some degree. 28,29 The results in (16) means that the rank of the system shown in ( 13) can be simplified. Then the rank of the system can be found in (17).…”

Section: Observability Analysis Of Gnss/ins Integration Systemmentioning

confidence: 99%

“…To solve the problem of unobservability of the heading angle and the instability of filtering in some scenes, Zero Heading angle Variation Constraint (ZHVC) and Sequential Adaptive Unscented Kalman Filter (SAUKF) algorithms were used. 16 The main idea is assuming that heading angle of a vehicle in a short period of time remains constant. Then a new measurement called heading angle variation which is the difference between the INS heading angle and the theoretical value was used to improve the accuracy of heading angle.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vehicle heading angle and IMU heading mounting angle improvement leveraging GNSS course angle

Liu

Xia³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Using vehicle chassis information can significantly improve the accuracy of GNSS/INS fusion system when GNSS signals are unavailable. The IMU mounting angles play an important role when fusing vehicle chassis information with GNSS/INS system. However, the IMU mounting angles cannot be directly measured conveniently. This paper proposed a new method to simultaneously improve the estimation accuracy of both the vehicle heading angle and the IMU heading mounting angle by leveraging GNSS course angle as an additional measurement. Firstly, an error state Kalman filter is constructed with state variables including the attitude errors, velocity errors, position errors, gyro, and acceleration bias errors, IMU mounting angle and vehicle velocity scale factor. The GNSS course angle is augmented to the Kalman filter as a measurement when the vehicle travels in straight line. Then, the observability analysis of the GNSS/INS/Onboard sensors fusion system is carried out and the results show that the observability of the system using GNSS course angle is better than that of only using vehicle velocity and non-holonomic constraint (NHC) as measurements if the heading mounting angle and pitch mounting angle are not zero. Finally, the experiment results show that the accuracy of the vehicle heading angle and the IMU heading mounting angle can be improved by 14% judging from the lateral velocity error of vehicle compared to that of only using vehicle velocity and NHC as measurements.

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Section: Observability Analysis Of Gnss/ins Integration Systemmentioning

confidence: 99%

Section: Observability Analysis Of Gnss/ins Integration Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle heading angle and IMU heading mounting angle improvement leveraging GNSS course angle

Liu

Xia³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…So, for the velocity and position parameters, we only take their horizontal parameters to establish the state equations. We take the errors of horizontal velocity, horizontal position, platform angles, accelerometer bias, and gyroscope bias as the state vectors [22], which can be written as…”

Section: Ins/gnss Integration System Modelmentioning

confidence: 99%

A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference

2021

Self Cite

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In this paper, a novel anti-jamming technique based on black box variational inference for INS/GNSS integration with time-varying measurement noise covariance matrices is presented. We proved that the time-varying measurement noise is more similar to the Gaussian distribution with time-varying mean value than to the Inv-Gamma or Inv-Wishart distribution found by Kullback–Leibler divergence. Therefore, we assumed the prior distribution of measurement noise covariance matrices as Gaussian, and calculated the Gaussian parameters by the black box variational inference method. Finally, we obtained the measurement noise covariance matrices by using the Gaussian parameters. The experimental results illustrate that the proposed algorithm performs better in resisting time-varying measurement noise than the existing Variational Bayesian adaptive filter.

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“…However, this method is only applicable to stationary or low-speed states. Inspired by vehicle kinematics, a novel method of heading angle estimation based on Zero-Heading angle-Variation-Constraint and Sequential-Adaptive Unscented Kalman Filter algorithms is proposed for solving the problem of heading angle unobservability and the instability of the filtering [23]. Owing to the presence of heading angular outputs from dual-antenna GNSS and INS systems, the mounting angle errors will reduce the heading angle accuracy [24].…”

Section: Introductionmentioning

confidence: 99%

Improving Vehicle Heading Angle Accuracy Based on Dual-Antenna GNSS/INS/Barometer Integration Using Adaptive Kalman Filter

Jiao,

Xu,

Chen

et al. 2024

Sensors

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High-accuracy heading angle is significant for estimating autonomous vehicle attitude. By integrating GNSS (Global Navigation Satellite System) dual antennas, INS (Inertial Navigation System), and a barometer, a GNSS/INS/Barometer fusion method is proposed to improve vehicle heading angle accuracy. An adaptive Kalman filter (AKF) is designed to fuse the INS error and the GNSS measurement. A random sample consensus (RANSAC) method is proposed to improve the initial heading angle accuracy applied to the INS update. The GNSS heading angle obtained by a dual-antenna orientation algorithm is additionally augmented to the measurement variable. Furthermore, the kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to enhance the accuracy of the measurement model. The heading errors in the open and occluded environment are 0.5418° (RMS) and 0.636° (RMS), which represent reductions of 37.62% and 47.37% compared to the extended Kalman filter (EKF) method, respectively. The experimental results demonstrate that the proposed method effectively improves the vehicle heading angle accuracy.

show abstract

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

Cited by 3 publications

References 20 publications

Vehicle heading angle and IMU heading mounting angle improvement leveraging GNSS course angle

Vehicle heading angle and IMU heading mounting angle improvement leveraging GNSS course angle

A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference

Improving Vehicle Heading Angle Accuracy Based on Dual-Antenna GNSS/INS/Barometer Integration Using Adaptive Kalman Filter

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