In the strapdown inertial navigation system (SINS), gyro drift will result in navigation errors. A new algorithm based on star sensor is proposed in this paper to estimate gyro drift. The paper analyzed the working principle of star sensor and the technique of estimating gyro drift. Gyro drift can be estimated through the high-precision attitude information provided by a star sensor. Kalman filter is used in the integrated navigation model. Simulation results show that the proposed algorithm can estimate gyro drift accurately and improve the precision of SINS.
Gyroscope is the key component in an Inertial Navigation System (INS). It depends on the precision of the INS. The nonlinear random drift error model based on autoregressive (AR) and genetic programming (GP) was established. The linear model is established based on AR technique. After that, the nonlinear model is built based on GP technique. The result indicates that the square error of the random gyro drift is reduced by 74.5%. The hybrid modeling method can effectively compensate the random gyro drift and improve the stability of the system.
Particle Swarm Optimization-Based Gyro Drift Estimation Method for Inertial Navigation System
