GNSS Position-Aided In-motion Coarse Alignment Method Based on Sliding Window Integral

In this paper, a global navigation satellite system (GNSS) position-aided in-motion coarse alignment method based on sliding window integral for strapdown inertial navigation system (SINS) is proposed. Because of the zero bias error in accelerometer and gyroscope measurements, full integral in the alignment time range will lead to the increasing cumulative error in the reference vector, eventually leading to the alignment error drift. In order to solve the cumulative error drift phenomenon caused by long-time full integral operation, the sliding window integral is utilized for sampling. In sliding window integral sampling, the current moment vector is only related to the first
$N$
sampling moments. Therefore, the bias errors in accelerometer and gyroscope measurements outside the sliding window will not accumulate in the current vector, which improves the accuracy of the reference vector. To reduce the influence of pseudo-measurement noise, an improved quaternion Kalman filtering (IQKF) algorithm is proposed to improve pseudo-measurement. The noise term of IQKF is the accumulation of the original square sum. Since the noise is a small, the magnitude of the noise can be effectively reduced after the square. Simultaneously, because the measurement noise of the system is complex, the measurement adaptive Kalman filtering algorithm is adopted. To verify the performance of the proposed method, an in-motion coarse alignment simulation is carried out. The results show that the proposed method can effectively reduce the drift phenomenon caused by the carrier's maneuvering motion and sensor zero bias error, and the convergence rate of the proposed method is faster than that of the compared methods.