Understanding deep-water striation patterns and predicting the waveguide invariant as a distribution depending on range and depth.

The Waveguide Invariant (WI) theory has been introduced to quantify the orientation of the intensity interference patterns in a range-frequency domain. When the sound speed is constant over the water column, the WI is a scalar with the canonical value of 1. But, when considering shallow waters with a stratified sound speed profile, the WI ceases to be constant and is more appropriately described by a distribution, which is mainly sensitive to source/receiver depths. Such configurations have been widely investigated, with practical applications including passive source localization. However, in deep waters, the interference pattern is much more complex and variable. In fact the observed WI varies with source/receiver depth but it also varies very quickly with source-array range. In this paper, the authors investigate two phenomena responsible for this variability, namely the dominance of the acoustic field by groups of modes and the frequency dependence of the eigenmodes. Using a ray-mode approach, these two features are integrated in a WI distribution derivation. Their importance in deep-water is validated by testing the calculated WI distribution against a reference distribution directly measured on synthetic data. The proposed WI derivation provides a thorough way to predict and understand the striation patterns in deep-water context.

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

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.

Source depth discrimination with a vertical line array.

Source depth estimation with a vertical line array generally involves mode filtering, then matched-mode processing. Because mode filtering is an ill-posed problem if the water column is not well-sampled, concerns for robustness motivate a simpler approach: source depth discrimination considered as a binary classification problem. It aims to evaluate whether the source is near the surface or submerged. These two hypotheses are formulated in terms of normal modes, using the concept of trapped and free modes. Decision metrics based on classic mode filters are proposed. Monte Carlo methods are used to predict performance and set the parameters of a classifier accordingly.

Model-based adaptive 3D sonar reconstruction in reverberating environments.

In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.