Abstract
Terrain-aided navigation (TAN) technology can provide underwater vehicles (UVs) with drift-free positioning information by correlating the bathymetric sequences measured along the track with the prior terrain map. Focusing on the positioning accuracy and robustness of the tracking stage in TAN, a terrain matching algorithm consisting of improved particle filter (PF) and the affine transformation is proposed. Firstly, the conditional proposal distribution in the regularized particle filter (RPF) is available from the ensemble Kalman filter (EnKF) updates based on historical observations rather than the state transition probability density. Secondly, the positioning information estimated by the improved RPF is used as a reference set to perform an affine transformation on the inertial waypoints. Finally, the matching sequence is moved in a sliding window to achieve a recursive estimation of the UV position. The results of vehicle field experiments on the actual digital map with 10 m resolution demonstrate that the optimized matching method outperforms the RPF algorithm in terms of mean and variance of positioning errors.
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