Source localization with broad-band matched-field processing in shallow water

Analytic expressions for the first order bias and second order covariance of a maximum-likelihood estimate (MLE) are applied to the problem of localizing an acoustic source in range and depth in a shallow water waveguide with a vertical hydrophone array. These expressions are then used to determine necessary conditions on sample size, or equivalently signal-to-noise ratio (SNR), for the localization MLE to become asymptotically unbiased and attain minimum variance as expressed by the Cramer-Rao lower bound (CRLB). These analytic expressions can be applied in a similar fashion to any ocean-acoustic inverse problem involving random data. Both deterministic and completely randomized signals embedded in independent and additive waveguide noise are investigated. As the energy ratio of received signal to additive noise (SANR) descends to the lower operational range of a typical passive localization system, source range and depth estimates exhibit significant biases and have variances that can exceed the CRLB by orders of magnitude. The spatial structure of the bias suggests that acoustic range and depth estimates tend to converge around particular range and depth cells for moderate SANR values.

Section: Waveguide Signal and Noise Modelsmentioning

confidence: 99%

Necessary conditions for a maximum likelihood estimate to become asymptotically unbiased and attain the Cramer–Rao lower bound. II. Range and depth localization of a sound source in an ocean waveguide

Thode

Zanolin

Naftali

et al. 2002

“…Previous studies 3,4,[32][33][34]36 have demonstrated that matched-field localization can be significantly improved by taking the incoherent average of the ambiguity surfaces obtained for individual frequencies. Figure 22 presents performance results for ambiguity surfaces incoherently averaged over the four tonals.…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…While the techniques vary from simple recalculations for each combination of parameters to complex nonlinear search techniques such as simulated annealing or genetic algorithms, successful inversions have been performed for parameters such as under-ice reflection amplitudes and phases, 22 ocean sound-speed structure, [23][24][25][26] bottom properties, 5,27-30 array tilt, 15 and array element locations. 31 The purpose of this study is to obtain an optimal replica model for a shallow-water testbed off the coast of San Diego, California, thus providing a ''ground truth'' baseline for ongoing MFP studies [32][33][34][35][36] in this area. This is accomplished via an analysis of nearly constant-water-depth multi-tone sourcetow data recorded on a vertical line array ͑VLA͒ during the first Shallow-Water Evaluation Cell Experiment ͑SWellEX-1͒, which took place in August 1993.…”

Section: Introductionmentioning

confidence: 99%

Matched-field replica model optimization and bottom property inversion in shallow water

Baxley

Booth

Hodgkiss

2000

Matched-field replica models based on an inaccurate knowledge of geoacoustic parameters such as bottom attenuation, shear, and interfacial sound-speed discontinuities, can predict an incorrect number of propagating modes for a shallow-water channel. The resulting degradation in the matched-field ambiguity surface can be substantially reduced by obtaining optimal replica models via modal-sum-limit optimization or bottom-property inversion. The use of these techniques for multi-tone (70, 95, 145, and 195 Hz) source-tow data recorded near San Diego during the first Shallow-Water Evaluation Cell Experiment (SWellEX-1) significantly increased matched-field correlation levels and improved source localization relative to results obtained with a previous nonoptimized model. The predicted number of propagating modes was also reduced substantially. The inversion for bottom properties (attenuation, interfacial sound-speed discontinuities, no shear) provided sediment attenuation estimates which agree well with Hamilton's models and were an order-of-magnitude greater than that used in the nonoptimized model, which accounts for the reduction in the number of modes. A simulated modal decomposition using the inverted optimal replica model verifies the number of modes predicted by the modal-sum-limit optimization.

“…29 The data were generated for a frequency of 150 Hz, a source range and depth of 2 km and 100 m, and an ocean depth of 216.5 m. The first receiver was at a depth of 50 m; all phones were vertically separated with a spacing of 10 m. The environment was that of SWellEX-96. [30][31][32][33] Considering no noise, there was full control of the value ͑equal to 1͒ and location of the global maximum of the ambiguity surface. A sketch of the environment is shown in Fig.…”

Section: Tabu For Matched-field Processing Using Synthetic Datamentioning

confidence: 99%

Tabu for matched-field source localization and geoacoustic inversion

Michalopoulou

Ghosh-Dastidar

2003

Tabu is a global optimization technique that has been very successful in operations research. In this paper, a Tabu-based method is developed for source localization and geoacoustic inversion with underwater sound data; the method relies on memory to guide the multiparameter search. Tabu is evaluated through a comparison to simulating annealing. Both methods are tested by inverting synthetic data for various numbers of unknown parameters. Tabu is found to be superior to the simulated annealing variant implemented here in terms both of accuracy and efficiency. Inversion results from the SWellEX-96 data set are also presented.