The accuracy of the north azimuth measured using the magnetic levitation gyroscope (GAT) declines owing to the influence of the periodic errors of GAT signals induced by the systematic error of the gyro rotor system and the influence of external environment. To address this issue, this paper proposes a novel methodological strategy based on continuous wavelet transform (CWT) and singular spectrum analysis (SSA) to process GAT periodic errors and improve the accuracy of north-seeking. Firstly, CWT is used to process a large number of GAT signals to obtain the statistical characteristics of the periodic errors. Subsequently, the reconstructed components (RCs) of the GAT signals are obtained using SSA. After detecting and grouping the periodic terms contained in each RC using CWT, the periodic errors in the GAT signals are clearly separated. Finally, the effectiveness of this method was verified by comparing our north azimuths with those measured using the high-precision global navigation satellite system (GNSS) baseline. Our results indicated that the periodic errors in GAT signals can be accurately divided into the high frequency periodic error and the low frequency periodic error, and both periodic errors can be clearly detected and separated. After processing, the root mean square error of the GAT rotor currents and the absolute difference between the gyro and high-precision GNSS north azimuths were enhanced by 22.6% and 43.2%, respectively. The method presented in this paper to process periodic errors is suitable for use in the preprocessing of GAT signals.

The signal measured by the maglev gyro sensor is sensitive to the influence of the instantaneous disturbance torque caused by the instantaneous strong wind or the ground vibration, which reduced the north-seeking accuracy of the instrument. To address this issue, we proposed a novel method combining the heuristic segmentation algorithm (HSA) and the two-sample Kolmogorov-Smirnov (KS) test (named HSA-KS method) to process the gyro signals and improve the north-seeking accuracy of the gyro. There were two key steps in the HSA-KS method: (i) all the potential change points were automatically and accurately detected by HSA, and (ii) the jumps in the signal caused by the instantaneous disturbance torque were quickly located and eliminated by the two-sample KS test. The effectiveness of our method was verified through a field experiment on a high-precision global positioning system (GPS) baseline at the 5th sub-tunnel of the Qinling water conveyance tunnel of the Hanjiang-to-Weihe River Diversion Project in Shaanxi Province, China. Our results from the autocorrelograms indicated that the jumps in the gyro signals can be automatically and accurately eliminated by the HSA-KS method. After processing, the absolute difference between the gyro and high-precision GPS north azimuths was enhanced by 53.5%, which was superior to the optimized wavelet transform and the optimized Hilbert-Huang transform.