Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization

Highly-reverberate underwater environments pose challenges for conventional localization techniques due to the highly non-linear nature of reflective surfaces, multi-path, and scattering fields. In this paper, we compare different machine learning methods for passive localization and tracking of single, non-stationary, underwater acoustic sources using multiple underwater acoustic vector sensors. We incorporate ordinal classification for localization in a novel approach to acoustic localization and compare the results with other standard methods. Real-world experiments demonstrate that both categorical and ordinal classification using deep LSTM networks significantly reduce localization error.