LONG BASELINE POSITIONING ALGORITHM BASED ON IMPROVED UNTRACED KALMAN FILTER
In complex water environments,the autonomous underwater vehicle(AUV)uses an acoustic navigation system to navigate autonomously and ensure accurate positioning.An improved untracked Kalman filter(AUKF)long baseline positioning algorithm is proposed to solve the problem of positioning accuracy loss caused by external noise in underwater acoustic environment.Based on the untraceable Kalman algorithm(UKF),the forgetting factor was introduced,and the new measurement data were used to dynamically adjust the measurement covariance matrix and process covariance matrix,which could effectively avoid the cumulative error caused by long-term operation.The experimental results show that when AUV runs along two different trajectories,the root mean square error of AUKF algorithm is the lowest,which is 2.901 1 and 19.221 5,respectively.It shows that this algorithm has high positioning accuracy and is suitable for long time underwater positioning with high precision.
万方数据
维普
