A Data-Driven Matching Error Compensation Framework for Underwater Gravity Aided Inertial Navigation

Continuity and reliability are critical for underwater gravity aided inertial navigation, while variations in gravity field suitability, sensor noise, and environmental disturbances can degrade gravity matching and navigation performance. To address this issue, a data-driven matching error compensation framework is proposed for gravity aided inertial navigation. Within this framework, a Hampel filter identifies unreliable gravity matching outputs based on local temporal consistency, and a CNN–BiLSTM–Attention model predicts compensated position increments for the flagged updates. The model maps INS position increments and measured gravity anomaly sequences to reliable gravity matching increments through local feature extraction, temporal modeling, and attention-based weighting, with offline training and online deployment. Reliable training samples were selected offline using reference trajectories, with synchronized GNSS positions serving only as the reference for sample screening in the marine experiments. Experiments were conducted across five simulated gravity field regions with five gravity matching algorithms and along three measured trajectories acquired using two types of marine gravimeters. In the marine experiments, the proposed method achieved mean APE-O values of 1.60, 1.06, and 1.06 n miles on L6, L7, and L8, respectively. The improvement was most evident on L6 with relatively extended error intervals, while the conventional RBIM method achieved comparable performance on the more regular and localized L8 error interval. Across the evaluated datasets, the proposed framework provided effective compensation for degraded gravity matching updates and improved the continuity of position corrections.