Kinetic Model-Constrained Robust In-Motion Alignment for Projectiles SINS/GNSS

Rapid and accurate initial alignment is a critical prerequisite for the performance in integrated navigation of strapdown inertial navigation system (SINS) and global navigation satellite system (GNSS), particularly in guided projectiles operating under in-motion conditions. However, conventional op...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 25; no. 20; pp. 38847 - 38856
Main Authors: Gao, Ning, Chen, Xiyuan, Yan, Zhe
Format: Journal Article
Language:English
Published: New York IEEE 15.10.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Adaptive filters
Alignment
Anomalies
Ballistic trajectories
Constraints
Convergence
Errors
Estimation
Extended Kalman filter
Extended Kalman filter (EKF)
Force
Global navigation satellite system
guided projectiles
Gyroscopes
Inertial navigation
Inertial platforms
initial alignment
Microelectromechanical systems
model constraints
Navigation systems
optimization-based in-motion alignment (OBIA)
Physical simulation
Projectiles
Robustness
Robustness (mathematics)
Strapdown inertial navigation
vector construction
Vectors
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:Rapid and accurate initial alignment is a critical prerequisite for the performance in integrated navigation of strapdown inertial navigation system (SINS) and global navigation satellite system (GNSS), particularly in guided projectiles operating under in-motion conditions. However, conventional optimization-based in-motion alignment (OBIA) methods often suffer from slow convergence and reduced accuracy in the presence of degraded GNSS signals and the cumulative errors of low-cost microelectromechanical system (MEMS)-based inertial measurement unit (IMU). To address these challenges, this article introduces a novel kinetic model-constrained robust in-motion alignment approach tailored for projectile applications. The proposed method integrates a simplified ballistic trajectory model into an extended Kalman filter (EKF) to constrain GNSS measurements, while innovation-based anomaly detection and an adaptive filtering strategy enhance robustness against GNSS outliers. Furthermore, a sliding-window integration scheme is employed to suppress drift errors from the MEMS-based IMU. Semi-physical simulation experiments under both nominal and degraded GNSS conditions demonstrate that the proposed method significantly outperforms conventional OBIA techniques in terms of convergence speed, alignment accuracy, and resistance to interference. These findings validate the proposed approach as an effective solution for rapid and robust initial alignment in time-critical projectile navigation scenarios.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2025.3607475