Using the trapped energy ratio for source depth discrimination with a horizontal line array: Theory and experimental results

Abstract

The problem of acoustic source depth discrimination was introduced as a way to get basic information on source depth in configurations where accurate depth estimation is not feasible. It is a binary classification problem, aiming to evaluate whether the source is near the surface or submerged. Herein, the classification relies on a signal measured with a horizontal line array in shallow water. Knowing the source-array distance is not required but the source bearing has to be close to the array endfire. Signal processing relies on a normal-mode propagation model, and thus requires prior knowledge of the mode characteristics. The decision relies on an estimation of the trapped energy ratio in mode space. The performance is predicted with simulations and Monte Carlo methods, allowing one to compare several estimators based on different mode filters, and to choose an appropriate decision threshold. The impact on performance of frequency, noise level, horizontal aperture, and environmental mismatch is numerically studied. Finally, the approach is validated on experimental data acquired with a horizontal line array deployed off the coast of New Jersey, and the impact of errors in the environmental model is illustrated. The investigated approach successfully identifies a surface ship and a submerged towed source.

Surface and underwater acoustic target recognition using only two hydrophones based on machine learning

“…This method was used for moving sources by estimating mode wavenumbers and depth functions from the data directly. Conan et al (2017) achieved source depth identification using captured energy ratio, based on horizontal linear array. The experiment successfully identified the surface combatant and the underwater towing source.…”

Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

“…This is another example where a simulated sound field based on an acoustic propagation model is mainly used (Conan et al, 2016;Conan et al, 2017;Liang et al, 2018). In particular, Conan et al (Conan et al, 2017) conducted a study to distinguish the depth of a sound source by using the propagation characteristics of sound waves in a specified environment. In their study, the normal mode method was used, which is one of the representative acoustic propagation models.…”

Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning

“…Considering that accurate depth estimation is not feasible, it may be a good idea to reformulate the depth estimation problem as a binary classification problem. A few researchers [1,9,10,11,12,13,14,15] have studied the binary classification of surface and underwater sources with model-driven methods. Premus [1] originally introduced a statistical algorithm involving the scintillation index for the source depth discrimination.…”

“…Two hypotheses for the low-order mode subspace and the high-order model subspace were tested for the source depth discrimination. A similar study was performed by Conan et al [10]. They defined decision metrics as the ratio of the trapped energy by low-order modes to the total energy, where the mode coefficients were extracted from the horizontal line array (HLA) data using three types of mode-filtering methods: The matched filter, least-square estimator, and regularized-least-squares estimator.…”