An algorithm of strapdown inertial navigation system/Doppler velocity log(SINS/DVL)integrated navigation assisted by attention-gated recurrent unit(Attention-GRU)is proposed to address the problem of degraded positioning accuracy caused by temporary failures of DVL in special terrains.During effective DVL measurements,the Attention-GRU neural network is trained by using SINS/DVL integrated navigation information.In the event of DVL failure,the trained Attention-GRU neural network predicts the DVL velocity to assist in correcting the SINS results.Simulation results demonstrate that when DVL is faulty,the Attention-GRU method reduces the average velocity error by 71.35%and 3.48%,and the average position error by 34.76%and 1.74%,respectively,compared with pure inertial navigation and GRU in constant velocity motion.During motion state changes,the Attention-GRU method reduces the average velocity error by 58.45%and 14.67%,and the average position error by 9.82%and 2.27%,respectively,compared with pure inertial navigation and GRU.
NETL
NSTL
万方数据
