Gradient Descent Optimization-Based Self-Alignment Method for Stationary SINS

This is the authors' abstract. We don't add key points for this paper.

The self-alignment process for the strapdown inertial navigation system (SINS) is performed mainly to achieve an accurate initial attitude only using the measurements from the inertial measurement unit (IMU). Different from conventional self-alignment methods, a gradient descent optimization-based SINS self-alignment method, which can determine the initial attitude without using the latitude information, is proposed in this paper. According to certain geometry constraints of the earth rate vector in the navigation frame, we use the measurements from the IMU to represent the earth rate vector instead of directly using the latitude information. To overcome the sensor noise disturbance, we also construct a quaternion-based nonlinear objective function with the estimated earth rate vector, which is formulated as seeking the least square solution of a Wahba problem. Thus, we employ the gradient descent optimization to achieve the optimal solution of the nonlinear objective function. The simulation and experiment results verify the alignment performance and flexibility of the proposed self-alignment method for SINS stationary alignment without using a priori local latitude information.