Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

Zhang, Fangfang; Wang, Xiaodong; Lu, Hao; Hao, Zhaojun; Changchao, Gu

doi:10.1155/2021/7398706

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…In equation (18), varis the number of windows at moment k, N min is 20, N max is 200, and S min is 0.1.…”

Section: Introduction Of Window Opening Methodmentioning

confidence: 99%

“…Lyu designed an adaptive shared-factor integrated navigation information fusion technique scheme in the literature [17], combining SINS, GNSS and vehicle dynamic model (VDM) multi-source sensors to establish an incomplete constraint-based VDM and GNSS measurement model, and the experimental results show that the algorithm has high navigation accuracy and fault tolerance. Zhang proposed an adaptive robust cubature Kalman filtering (CKF) algorithm in the literature [18] for the coarse and inaccurate noise statistical properties of the observations, and experiments show that the algorithm can enhance the stability and improve the estimation accuracy and convergence speed of the filter. Chen proposed an adaptive extended Kalman filter (EKF) algorithm in the literature [19] to eliminate the improper selection of noise covariance, and experiments show that the algorithm effectively improves the positioning accuracy of INS/GNSS.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Wang

Cheng

et al. 2023

Meas. Sci. Technol.

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Traditional adaptive Kalman filtering algorithms based on innovation are often used to solve the problem of reduced or even divergent filtering estimation accuracy under abnormal measurement noise. However, these algorithms are usually characterized by difficulties in selecting window width and window weight, which cannot simultaneously take into account the filtering tracking sensitivity and filtering accuracy.In this paper, an adaptive Kalman filtering algorithm based on maximum likelihood estimation is proposed, which determines the window size and window weight size under the kth moment by designing a window adaptive selection function and a weight function to change the innovation covariance at the kth moment, which in turn changes the measurement noise covariance at the kth moment, so that the measurement noise covariance is no longer a fixed single value, but can better adapt to the changes in the environment, reflecting good adaptive characteristics. The simulation results based on GPS/SINS integrated navigation system demonstrate that the new filtering algorithm of this paper reflects higher filtering accuracy and stronger robustness under the carrier in multiple motion states and accompanied by time-varying measurement noise interference. Compared with the traditional adaptive Kalman filtering algorithm based on innovation, the accuracy of attitude angle estimation error under this method is improved by 119.97%; the accuracy of velocity estimation error is improved by 264.42%; the accuracy of position estimation error is improved by 156.69%.

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“…In equation (18), varis the number of windows at moment k, N min is 20, N max is 200, and S min is 0.1.…”

Section: Introduction Of Window Opening Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Wang

Cheng

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Currently, there are two main categories of traditional alignment methods based on misalignment angles-small misalignment angles linear alignment and large misalignment angles nonlinear alignment [2]. Linear alignment models and linear filtering algorithms for small misalignment angles are well established, while research on the alignment problems for large misalignment angles has mainly focused on nonlinear models and nonlinear filtering algorithms [3]. However, these approaches can lead to errors in model linearization, increased computational complexity, and reduced filtering accuracy for large misalignment angles.…”

Section: Introductionmentioning

confidence: 99%

An Improved Initial Alignment Method Based on SE2(3)/EKF for SINS/GNSS Integrated Navigation System with Large Misalignment Angles

Sun,

Chen,

Cui

2024

Sensors

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This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE2(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE2(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level.

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Comparative Study of CKF and UKF in Dimensionality Reduction Fast Transfer Alignment of Inertial Navigation Systems of Shipborne Equipment

Wang,

Xu,

2024

2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE)

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Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

Cited by 3 publications

References 14 publications

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

An Improved Initial Alignment Method Based on SE2(3)/EKF for SINS/GNSS Integrated Navigation System with Large Misalignment Angles

Comparative Study of CKF and UKF in Dimensionality Reduction Fast Transfer Alignment of Inertial Navigation Systems of Shipborne Equipment

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