Source localization in the presence of internal waves

Abstract

The possibility of determining the location of an acoustic source in the presence of internal waves is investigated. Source localization problems require environmental information as inputs. Internal waves cause uncertainties in the sound speed field. In previous work ͓J. Acoust. Soc. Am. 90, 1410-1422 ͑1991͔͒, it was found that a source can often be localized in an uncertain environment by including environmental parameters in the search space and tweaking them, but usually not determining their true values. This is possible due to a parameter hierarchy in which the source position is more important than the environmental parameters. The parameter hierarchy is shown to also apply to uncertainties associated with internal waves. Due to differences in the nature of the parameter space, this problem is solved with a statistical approach rather than a parameter search technique such as simulated annealing.

Source localization in the presence of gross sediment uncertainties

“…9 to solve the focalization problem. This involves randomly selecting realizations of the environment, constructing ambiguity surfaces by the method of phase conjugation and back propagation, and constructing histograms of the locations of the main peaks in the ambiguity surfaces.…”

“…In the water column, we use the canonical profile 11 that was used in Ref. 9 for each of the examples. We work in FIG.…”

“…Focalization has recently been applied to localize a source in the presence of internal waves. 9 It was found that internal waves ultimately become a serious limiting factor for distant sources but that localization can be improved by including internal waves in the parameter space. In this paper, we show that focalization can also be effective in the presence of gross sediment uncertainties.…”

Experimental testing of the noise-canceling processor

“…The feasibility of matched-field processing was at one time in question due to the issue of environmental uncertainty, but it is often possible to determine the source location by varying the environmental parameters without necessarily determining their correct values. [4][5][6] Signal-to-noise ratio (SNR) is also a key factor, but an approach for canceling noise has provided promising results in simulations. 7 The noise-canceling processor has also been tested in simulations for problems involving environmental uncertainties.…”

Temporal coherence after multiple forward scattering through random three-dimensional inhomogeneities in an ocean waveguide

“…The resulting random multiple forward scattering causes significant fluctuations in the acoustic field, 1-8 leads to degradation in the temporal coherence of acoustic signals 9-11 and significant signal-dependent noise. 12 Understanding the properties of this signal-dependent noise is often critical to effectively employ acoustics to ocean remote sensing and communication, [13][14][15][16][17][18][19] as well as in the Acoustic Thermometry of Ocean Climate ͑ATOC͒. 10 Knowledge of the coherence time scale of a received signal is essential in ͑1͒ reducing the error of any measurement or estimate obtained from fluctuating acoustic field data by stationary averaging and ͑2͒ applying the fundamental coherent processing techniques of ocean acoustics, such as matched filtering, beamforming, matched-field and synthetic aperture processing.…”