An improved method of SINS/CNS integrated navigation algorithm based on dual quaternions with gyro error corrected online

In consideration of the high dimension problem in the existing SINS/CNS integrated navigation system based on dual quaternions, the observable degree analysis method of system states based on singular value decomposition is introduced. According to the analysis result, the state components with good observability are estimated and the bad ones are removed, thus the computation time of Kalman filter is significantly decreased and the real-time performance of SINS/CNS is improved. To ensure that inertial navigation can also maintain high accuracy over a period of time when celestial auxiliary information is lost, an improved method of SINS/CNS integrated navigation algorithm based on dual quaternions with gyro error corrected online is proposed. The Kalman filter estimates constant gyro drift with the aid of celestial information and utilizes the calibration value to compensate gyro output online. The simulation results demonstrate that the proposed method can realize the effective estimation of constant gyro drift. In addition, the accuracy of inertial navigation is improved effectively.