A New Kalman Filter-Based In-Motion Initial Alignment Method for DVL-Aided Low-Cost SINS

The inertial measurement unit (IMU) biases, DVL lever arm and installation misalignment angles between IMU and doppler velocity log (DVL) have a great influence on in-motion initial alignment for DVL-aided low-cost strap-down inertial navigation system (SINS). A new Kalman filter-based initial alignment method is proposed in this paper. To weaken the effects of IMU biases, DVL lever arm and installation misalignment angles between IMU and DVL, a closed-loop scheme is presented to simultaneous estimate and compensate these parameter errors and the body attitude matrix based on a linear state-space model, which improves the accuracy of vector observations. The constant matrix from initial body frame to initial navigation frame can be determined based on the vector observations by Davenport's q-method. Simulation results illustrate that, for the DVL-aided low-cost SINS, the alignment performance of the proposed initial alignment method is better than that of the compared initial alignment methods.

Automated summary

A new Kalman filter-based initial alignment method is proposed in this paper to improve the accuracy of vector observations by estimating and compensating parameter errors and body attitude matrix. Simulation results demonstrate that the proposed method outperforms compared initial alignment methods in terms of alignment performance for DVL-aided low-cost SINS.