Target detection and localization in shallow water: An experimental demonstration of the acoustic barrier problem at the laboratory scale

Abstract

This study demonstrates experimentally at the laboratory scale the detection and localization of a wavelength-sized target in a shallow ultrasonic waveguide between two source-receiver arrays at 3 MHz. In the framework of the acoustic barrier problem, at the 1/1000 scale, the waveguide represents a 1.1-km-long, 52-m-deep ocean acoustic channel in the kilohertz frequency range. The two coplanar arrays record in the time-domain the transfer matrix of the waveguide between each pair of source-receiver transducers. Invoking the reciprocity principle, a time-domain double-beamforming algorithm is simultaneously performed on the source and receiver arrays. This array processing projects the multireverberated acoustic echoes into an equivalent set of eigenrays, which are defined by their launch and arrival angles. Comparison is made between the intensity of each eigenray without and with a target for detection in the waveguide. Localization is performed through tomography inversion of the acoustic impedance of the target, using all of the eigenrays extracted from double beamforming. The use of the diffraction-based sensitivity kernel for each eigenray provides both the localization and the signature of the target. Experimental results are shown in the presence of surface waves, and methodological issues are discussed for detection and localization.

Sensitivity kernel for surface scattering in a waveguide

“…(10) and (9), one derives the expression for both the amplitude and travel time SSK of each eigenray path. As shown by Marandet et al, 13 the DBF-SSK for the normalized amplitude is numerically computed for any r 0 between the source and the receiver arrays as…”

“…For example, in underwater acoustics, travel time tomography has been recently developed with the sensitivity kernel 12 to predict the arrival-time change of the acoustic echoes. Similarly, a recent paper has experimentally shown that the sensitivity kernel for a local change in density 13 or sound speed 14 could be used for the localization of a target or a temperature anomaly in an ultrasonic waveguide. Both results apply to multipath propagation media (shallow or deep water ocean) in which the acoustic field is perturbed by a local volume change.…”

Localization of a small change in a multiple scattering environment without modeling of the actual medium

“…sensitivity kernel analysis. 22,23 But here the variation in the functional error is given in energy instead of amplitude.…”

Timelapse ultrasonic tomography for measuring damage localization in geomechanics laboratory tests

“…As such, the optimal subarray size results from a balance between resolution and robustness. [22][23][24] Large subarrays provide enhanced angular resolution. However, when there are strong heterogeneities close to the transducer arrays, as is often the case for geomaterials (where localized deformations may spread across the entire sample), beamforming performs better with shorter subarrays.…”