Velocity-Integral-Constraint-Based In-Motion Initial Alignment for Kalman Filter-Aided Low-Cost SINS

Initial alignment is a crucial prerequisite for the performance of underwater integrated navigation systems, particularly in terms of alignment speed and accuracy. The limited maneuverability of underwater vehicles and the significant biases inherent in low-cost micro-electromechanical system (MEMS)-inertial measurement units (IMUs) pose considerable challenges to the effectiveness of in-motion initial alignment (IMIA). To enhance the dynamic alignment performance of low-cost integrated navigation systems under conditions of limited maneuverability, a novel Kalman filter-assisted optimization alignment method that leverages velocity integral constraints is proposed. By constructing observations using the velocity integral spatial constraint relationship, the proposed method avoids the failure of acceleration observations due to insufficient maneuverability, providing a robust solution for dynamic alignment in such challenging scenarios. Additionally, a closed-loop correction strategy incorporating the Kalman filter technology is proposed to jointly estimate IMU biases and attitude errors, significantly enhancing the accuracy of observations in the dynamic alignment solution model. Simulation and field test results demonstrate that the proposed method offers significant improvements in alignment speed and accuracy, outperforming state-of-the-art methods for dynamic alignment of low-cost integrated navigation systems under conditions of limited maneuverability.