Underwater INS-LBL integrated navigation algorithm under non-gaussian noise
Autonomous underwater vehicles(AUV)arethe mainequipments for underwater operations,and obtaining accurate positioning is the prerequisite for AU Vs to complete tasks.Due to the fact that the system noise in actual environ-ment is non-Gaussian and it is difficult to obtain an exact model,further causing low positioning accuracy,an INS-LBL in-tegrated navigation algorithm based on maximum correntropy Kalman filter(MCKF)is proposed.Firstly,establish the integ-rated navigation model based on pseudorangebetween INS and LBL as the system measurement to reduce the acoustic delay.In addition,the MCKF is used to improve positioning accuracyand robustness under complex noise interference.Simulation results demonstrate that even innon-Gaussian noise environment,theproposed algorithm can suppress noise to ensure the high positioning accuracy.
maximum correntropy Kalman filterunderwater navigationlong baselineAUV
NETL
NSTL
万方数据
