Underwater acoustic localization via gradient-based optimization

Abstract

In underwater acoustic localization via matched-field-processing, given a propagation model and a suitable environmental parameterization, one searches for the location (of the transmitter or receiver) whose replica field is closest to the observed one. The high computational complexity of such non-gradient-based optimization methods renders them infeasible for many real-time scenarios, especially when an accurate solution is desired due to resolution of the search grid required, or as the search dimensionality increases (e.g., when it is necessary to optimize over uncertain environmental parameters such as sound speed or bathymetry). In this talk, we propose a ray-based, differentiable model for acoustic propagation that can be exploited in a gradient-based optimization for localization. For localization applications in which accurate times of arrival might not be available (e.g., due to the signal's relatively small bandwidth), the proposed method does not directly rely upon times of arrivals. Rather, it seeks the location (and possibly environmental parameters) that minimize the squared-error between the observed signal and its estimation via the differentiable model. We leverage the PyTorch optimization and auto-differentiation tools for the implementation and demonstrate successful localization on synthetic data in a dense multipath environment.