Strapdown Inertial Navigation System Initial Alignment Based on Group of Double Direct Spatial Isometries

Chang, Lubin; Qin, Fangjun; Xu, Jiangning

doi:10.1109/jsen.2021.3108497

Cited by 42 publications

(23 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, similar to the SE(3) based EKF for attitude estimation [7], the biases of the accelerometer and gyroscope can be considered to incorporate into matrix Lie group SE 4 (3) so that the orientation error can be considered for the biases in the inertial-integrated navigation. Furthermore, the theory is applied to more inertial-integrated navigation applications such as initial alignment [16], tightly couple integration, filter-based SLAM, etc.…”

Section: Discussionmentioning

confidence: 99%

$SE_2(3)$ based Extended Kalman Filtering and Smoothing Framework for Inertial-Integrated Navigation

Luo,

Guo,

You

et al. 2021

Preprint

View full text Add to dashboard Cite

The error representation using the straight difference of two vectors in the inertial navigation system may not be reasonable as it does not take the direction difference into consideration. Therefore, we proposed to use the SE 2 (3) matrix Lie group to represent the state of the inertial-integrated navigation system which consequently leads to the common frame error representation. With the new velocity and position error definition, we leverage the group affine dynamics with the autonomous error properties and derive the error state differential equation for the inertial-integrated navigation on the north-east-down local-level navigation frame and the earth-centered earth-fixed frame, respectively, the corresponding extending Kalman filter (EKF), terms as SE 2 (3)-EKF has also been derived. It provides a new perspective on the geometric EKF with a more sophisticated formula for the inertial-integrated navigation system. Furthermore, we propose a SE 2 (3)-based smoothing algorithm based on the SE 2 (3)-based EKF.

show abstract

Section: Discussionmentioning

confidence: 99%

$SE_2(3)$ based Extended Kalman Filtering and Smoothing Framework for Inertial-Integrated Navigation

Luo,

Guo,

You

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For INS initial alignment, this means that with the log-linear differential equation, the IEKF is applicable even with arbitrary large misalignments. This has been demonstrated with successful applications and promising results in [8,9,[12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 96%

“…In [7], Barrau and Bonnabel have provided the rigorous proof of the strong convergence of the loglinear differential equation in continuous time. However, it is undeniable that the log-linear differential equation is still derived with first-order approximation of the attitude error matrix in existing literatures [7,[11][12][13][14][15][16][17][18]. The promising results by IEKF with arbitrary large misalignments seemingly go against the common sense that the first-order linearization should be therefore only valid for small misalignments.…”

Section: Introductionmentioning

confidence: 99%

Log-linear Error State Model Derivation without Approximation for INS

Chang

Luo

2022

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

Through assembling the navigation parameters as matrix Lie group state, the corresponding inertial navigation system (INS) kinematic model possesses a group-affine property.The Lie logarithm of the navigation state estimation error satisfies a log-linear autonomous differential equation. These log-linear models are still applicable even with arbitrarily large initial errors, which is very attractive for INS initial alignment. However, in existing works, the log-linear models are all derived based on first-order linearization approximation, which seemingly goes against their successful applications in INS initial alignment with large misalignments. In this work, it is shown that the log-linear models can also be derived without any approximation, the error dynamics for both left and right invariant error in continuous time are given in matrix Lie group SE (3) for the first time. This work provides another evidence for the validity of the log-linear model in situations with arbitrarily large initial errors.

show abstract

“…Chang conducted in-depth research on nonlinear initial alignment methods [ 10 , 11 , 12 ]. In his research, the dynamic model based on nonlinear error equations was re-derived with consideration of the mismatch between the calculated navigation frame and the actual navigation frame [ 13 , 14 , 15 , 16 ]. The unself-alignment requires external information as the reference for the initial alignment [ 17 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

One-Step Initial Alignment Algorithm for SINS in the ECI Frame Based on the Inertial Attitude Measurement of the CNS

Tang

Bian

et al. 2022

Sensors

View full text Add to dashboard Cite

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6′. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3′.

show abstract

Strapdown Inertial Navigation System Initial Alignment Based on Group of Double Direct Spatial Isometries

Cited by 42 publications

References 46 publications

$SE_2(3)$ based Extended Kalman Filtering and Smoothing Framework for Inertial-Integrated Navigation

$SE_2(3)$ based Extended Kalman Filtering and Smoothing Framework for Inertial-Integrated Navigation

Log-linear Error State Model Derivation without Approximation for INS

One-Step Initial Alignment Algorithm for SINS in the ECI Frame Based on the Inertial Attitude Measurement of the CNS

Contact Info

Product

Resources

About