Attention-GRU 神经网络辅助的 SINS/DVL 组合导航算法

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.