An Improved Adaptive Filter Algorithm of SINS/GNSS Navigation Based on Allan Variance
In view of the problem that SINS/GNSS navigation accuracy degrades due to divergence of existing adaptive Kalman filter when both state noise and measurement noise change. An improved adaptive filtering algorithm based on Allan variance is proposed. On the basis of the improvement of adaptive filtering,Combined with the Allan variance estimation method,the measurement noise covari-ance matrix is calculated,overcoming the problem of noise parameter coupling and filtering divergence caused by singularity in high-di-mensional systems in adaptive filtering,and on the basis of judging the system state by using fault detection method,the forgetting factor is dynamically adjusted to track the noise characteristics more quickly. Compared to other improvement methods,it is simple and easy to be implemented. The average speed mean square error can be increased by 49.06％ compared to traditional Kalman and 27.19％ com-pared to Sage-Husa adaptive filtering. The average position mean square error can be increased by 41.12％ compared to traditional Kal-man and 19.79％ compared to Sage-Husa adaptive filtering.
万方数据
维普
