Self-calibration method for temperature errors in multi-axis rotational inertial navigation system

Rotation modulation technology of inertial navigation system brings navigation performance increasement without any new requirement on inertial sensors. However, device errors still make significant influence on navigation precision. Traditional temperature model identification methods cost plenty of time which reduce production efficiency. Therefore, it is necessary to study an effective solution decreasing temperature resulted errors for engineering application. The paper proposes a fast-self-calibration method for temperature errors. A continuous rotation scheme is designed to excite 21 errors inside of 10 minutes. Kalman Filter algorithm is applied to estimate 21 errors taking velocity errors and position errors as measurements. In order to identify temperature model, the rotation scheme is repeated ten times to estimate error parameters under different temperature. Due to the fast rotation scheme, temperature rising rate can be higher than traditional methods and calibration time is shortened. Finally, the method is verified by simulations and experiments.