An adaptive sparse sensing approach to shallow water acoustic channel estimation and tracking.

Abstract

The shallow water acoustic channel is challenging to estimate and track due to the ill‐conditioned nature of the problem, the need to optimize over a complex field, and the time‐varying nature of the channel coefficients. We have previously presented a geometric mixed norm approach to estimate and track the channel delays and delay‐Doppler spread function coefficients that exploit the often sparse distribution of the channel coefficients. In this work, we present the effectiveness of our approach over a range of field data collected at 15 m depth over ranges of 60, 200, and 1000 m at various wind conditions. We also compare the performance of the estimation and tracking algorithm against other sparse sensing approaches to shallow water acoustic channel estimation, e.g., the L1‐regularized least squares algorithm. We show that the choice of the sparsity factor λ, which is an essential design parameter in all sparse sensing methods, plays a critical role in the effectiveness of sparse reconstruction of real...