In underwater SINS/DVL integrated navigation,the instability and loss of DVL signal often occur due to external factors,which may easily lead to discontinuous positioning or weakened accuracy. In this paper,the data collected during the normal period of DVL are used as training samples,and the radial basis function neural network algorithm(RBF)is used to fill the signal during the period of DVL loss. To reduce the influence of system noise,two modes of extended Kalman filter(EKF)and adaptive fading Sage-Husa extended Kalman filter(SHEKF) are selected for integrated navigation calculation,and different calculation results are obtained. The analysis shows that RBF algorithm can be used to deal with the loss of DVL signal. Under the same conditions,SHEKF filter mode can get better calculation results,and the position error in the direction of E is reduced by about 50% compared with EKF filter.
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