Data-Driven Direct Localization

Architecture of the proposed deep neural network for data-driven direct localization. The model is comprised of three sub-models, which are initially trained individually to estimate range, azimuth and inclination.

This package contains the supplementary material for [1].

To download the package, click here.

[1] Weiss, A., Arikan, T., and Wornell G. W., “Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach”, accepted for publication in Proc. of IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP), June 2022. arXiv

[2] Weiss, A., Arikan, T., Vishnu, H., Deane, G. B., Singer, A. C. and Wornell, G. W., “A Semi-Blind Method for Localization of Underwater Acoustic Sources”, IEEE Trans. on Signal Processing, vol. 70, pp. 3090–3106, May 2022. arXiv