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Using relative channel impulse responses to train a machine learning algorithm for 2-D surface ship source localization

Underwater source localization using receiver arrays is often achieved with a purely model-based approached, i.e., matched-field processing using simulated replica-field. However, these approaches only yield reasonable predictions if the complicated ocean environment can be precisely described, which is often a daunting task. Alternatively, by turning to a data-driven approach, source localization can be achieved without the strong dependence on model-parameters. Recently, source localization in the Santa Barbara Channel (∼580 m depth, downward refracting profile) has been achieved via measurements of ships of opportunity, and machine learning classifiers. In this presentation, relative channel impulse responses, estimated via the ray-based blind deconvolution, are used to train a neural network to predict the latitude and longitude of a surface ship within the Santa Barbara Channel. Localization results for simulated and experiment data are presented and implications for future work are discussed.Underwater source localization using receiver arrays is often achieved with a purely model-based approached, i.e., matched-field processing using simulated replica-field. However, these approaches only yield reasonable predictions if the complicated ocean environment can be precisely described, which is often a daunting task. Alternatively, by turning to a data-driven approach, source localization can be achieved without the strong dependence on model-parameters. Recently, source localization in the Santa Barbara Channel (∼580 m depth, downward refracting profile) has been achieved via measurements of ships of opportunity, and machine learning classifiers. In this presentation, relative channel impulse responses, estimated via the ray-based blind deconvolution, are used to train a neural network to predict the latitude and longitude of a surface ship within the Santa Barbara Channel. Localization results for simulated and experiment data are presented and implications for future work are discussed.