Use of mode subspace projections for depth discrimination with a horizontal line array: Theory and experimental results

Abstract

A solution to the problem of acoustic source depth discrimination in a downward refracting, shallow-water ocean waveguide is presented for the case of a horizontal line array at endfire. The approach exploits the phenomenon of mode trapping, wherein a shallow acoustic source cannot excite the lowest order waveguide modes due to its evanescent amplitude dependence near the surface. The important implication of this "trapping" behavior is that, given sufficient spatial aperture, it provides a mechanism for differentiating a shallow acoustic noise source from one at depth. The method does not require array cant, or physical vertical aperture of any kind, but instead relies only on the sensitivity of a line array at endfire to differences in horizontal wave number to resolve low and high order mode subspace excitations. The only inputs to the algorithm are an approximate sound speed profile, water depth, and bottom type. The theoretical basis for the test statistic is first reviewed, followed by discussion of key requirements, and illustration of the concept using results from a RAM PE simulation for a downward refracting environment. Finally, the algorithm is experimentally demonstrated using data from a bottom-mounted HLA deployed in the moderately cluttered continental shelf environment of the Florida Straits.

Depth Discrimination for Low-Frequency Sources Using a Horizontal Line Array of Acoustic Vector Sensors Based on Mode Extraction

“…Simulations showed that this technique could discriminate between a clutter source in the water column and one on the seabed. Premus et al achieved source discrimination by using information about the source range, but it is difficult to obtain such information accurately with passive sonar [13]. The data results of the method demonstrated that the test statistic in [13] could discriminate between (i) a source towed at a depth of 100 m and (ii) the naturally radiated signatures of two surface ships in the same environment.…”

“…Premus et al achieved source discrimination by using information about the source range, but it is difficult to obtain such information accurately with passive sonar [13]. The data results of the method demonstrated that the test statistic in [13] could discriminate between (i) a source towed at a depth of 100 m and (ii) the naturally radiated signatures of two surface ships in the same environment. Li separated the modes by using the beamforming in mode space [14], whereupon matched-mode processing was used to estimate the source depth.…”

“…In this section, we compare the performance of the proposed method with those of two other existing depth-discrimination methods, namely the mode subspace projection (MSP) method [13] and the modal domain beamforming (MDB) method [14]. The simulation environments are the same as that in Section 5.…”

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.…”

Source depth discrimination with a vertical line array

“…Source depth discrimination in such a context was originally addressed by Premus and coworkers. [1][2][3] Their work was based on the concept of trapped and free modes, which is particularly relevant in a downward-refracting shallow water environment. The tested hypotheses were that the deterministic part of the signal was due either only to free modes (surface source hypothesis) or only to trapped modes (submerged source hypothesis).…”