A two-step network assisted by direction-finding error underwater acoustic bearings-only localization method

This paper is concerned with the underwater acoustic distributed bearings-only passive localization. To overcome the problems of low localization accuracy over long distances and the localization results being easily affected by the initial values, a two-step fully connected neural network assisted by direction-finding error (DFE-TS-FCNN) bearings-only localization method is presented. The neural network is used to improve localization accuracy over long distances as well as eliminate the influence of initial values. Target direction measurements and standard deviation estimates of direction-finding error are used as input features. A two-step network structure is used to prevent overfitting of the networks. The target region is determined by the classification network, and subsequently estimated by the localization network corresponding to that specific region. In the Monte Carlo simulation experiment, similar localization accuracy is achieved compared to the iterative weighted least-squares algorithm and the iterative total least-squares algorithm under close distances, while simultaneously localization accuracy is improved over long distances. When the root mean square error (RMSE) is less than 2.5 km, the furthest directional distance increases from 12.6 km to 22.7 km compared with traditional algorithms. Excellent localization results have also been demonstrated in real data.