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In SINS, the inertial components are directly mounted on the carrier.The error can be divided into deterministic error and random drift error (dynamic error), in which, the formercan be compensated. In this case, the initial alignment of the pure static base can achieve a high accuracy. In the practical application, the dynamic error is directly reflected in the inertial device because of influence of the external environment (wind, vibration and disturbance, etc.). At this time, the error model is not linear. Under this situation, unscented Kalman filter (UKF) and adaptive unscented Kalman filter (AUKF) were designed respectively. We introduced the principle of adaptive estimation into the original UKF algorithm, adjusted the contribution of the kinetic model to navigation solution. AUKF algorithm can automatically balance the weight ratio of state and observation information in filtering to adjust the covariance of state vector and observation vector in real-time, thereby to improve the system performance. Experimental results showed that the use of adaptive UKF algorithm in comparion with the normal UKF algorithm can obtain better accuracy and reliability of self alignment.

Optimization Analysis of adaptive UKF filtering algorithm in self alignment of SINS