Source localization in a time-varying ocean waveguide

Abstract

One of the most stringent impairments in matched-field processing is the impact of missing or erroneous environmental information on the final source location estimate. This problem is known in the literature as model mismatch and is strongly frequency dependent. Another unavoidable factor that contributes to model mismatch is the natural time and spatial variability of the ocean waveguide. As a consequence, most of the experimental results obtained to date focus on short source-receiver ranges (usually <5 km), stationary sources, reduced time windows and frequencies generally below 600 Hz. This paper shows that MFP source localization can be made robust to time-space environmental mismatch if the parameters responsible for the mismatch are clearly identified, properly modeled and (time-)adaptively estimated by a focalization procedure prior to MFP source localization. The data acquired during the ADVENT'99 sea trial at 2, 5, and 10 km source-receiver ranges and in two frequency bands, below and above 600 Hz, provided an excellent opportunity to test the proposed techniques. The results indicate that an adequate parametrization of the waveguide is effective up to 10 km range in both frequency bands achieving a precise localization during the whole recording of the 5 km track, and most of the 10 km track. It is shown that the increasing MFP dependence on erroneous environmental information in the higher frequency and at longer ranges can only be accounted for by including a time dependent modeling of the water column sound speed profile.

Acoustic observatories for ocean tomography: Multi-array matched-field tomography

“…A similar concept was exploited in the focalization processor, whose aim was to allow range-depth source localization with an alternative physical model [8]. Since, in past studies successful acoustic modeling with a range-independent model was achieved with source-receiver ranges of up to 10 km [9], it is acceptable to initialize the algorithm with the hypothesis in Phase 1. Another remark is related to the geometry of the acoustic system: in order to increase the field diversity, one should deploy emitters and/or receivers at different depths, and set up acoustic paths with different lengths.…”

An experimental demonstration of blind ocean acoustic tomography

“…Examples are given in recent publications by the authors. 18,20,26,27 The optimization technique for reducing the number of forward computations was based on a genetic algorithm ͑GA͒. Principles of GA are now well known in the underwater acoustic community and elsewhere, and various strategies have been widely used in practice with positive results.…”

“…17,18 In other studies the parameters under search simultaneously included geoacoustic, water column, and geometric parameters both known and unknown. 19,20 The term generally used for approaches performing multiparameter search including both known and unknown parameters is focalization, and was first proposed by Collins et al 21 in the context of range-depth source localization. So, in our case the problem to be addressed under the scope of this paper involves an environmental and geometrical focalization procedure with a random source of unknown spectra.…”

Dynamic Estimation of the Sound-Speed Profile from Broadband Acoustic Measurements

“…EOF are basis functions that can be obtained from a database and are very efficient to reduce the number of data points to be estimated. The use of EOF has already proved its efficiency in sound-speed profile inversion problem ( [10], [11] and many others).…”