Detection of low-level broadband signals using adaptive matched-field processing with horizontal arrays

Booth, Newell O.; Schey, Phil; Hodgkiss, William S.

doi:10.1121/1.420793

Cited by 2 publications

(3 citation statements)

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“…The SWellEX-96 data were obtained in shallow waters off the Southern California coast in May 1996. 6 The experiment provided a large amount of highquality data for various geometrical and environmental configurations. In this study, vertical array data are analyzed from a radial incoming track ͑event S107, 14 May 23 ͒ over a gently downsloping bottom from about 150 m at the start of the radial to 215 m at the array ͑Fig.…”

Section: Swellex-96 Datamentioning

confidence: 99%

“…The basement compressional attenuation varied linearly from 0.1 to 5.0 dB/ to the bottom of the computational grid at 500 m. The waveguide is divided into five segments ͑six points͒ to account for the range-dependent bathymetry. The sound speed profile in the water was measured in the experiment 6 and is not included in the parameter search. The geometric parameters ͑source range, source depth, and array depth͒ are also included in the search.…”

Section: Swellex-96 Datamentioning

confidence: 99%

“…In Sec. IV, the method is applied to data from the SWellEX-96 experiment 6 to estimate both the source position and the geoacoustic properties along a radial track.…”

Section: Introductionmentioning

confidence: 99%

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Range-dependent matched-field inversion of SWellEX-96 data using the downhill simplex algorithm

Musil¹,

Chapman²,

Wilmut³

1999

The Journal of the Acoustical Society of America

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Matched-field inversion ͑MFI͒ techniques have been applied for effective and efficient estimation of geoacoustic parameters of the ocean bottom. This paper presents a new tomographic MFI method for use in range-dependent environments. The MFI correlates modeled data with measured data and uses a search algorithm to determine model parameter values that maximize the correlator. In the present method, a parabolic equation propagation model is used to compute replica fields to account for mode coupling in the environment. The search algorithm is a two-stage hybrid method that combines an initial global component and a final local component. The first stage employs a random search to determine Nϩ1 parameter sets with the best correlations, where N is the number of parameters being determined. In the next stage the Nϩ1 sets are used as inputs to the local downhill simplex algorithm. The algorithm is shown to perform well for simulated vertical line array data for an environment representative of the SWellEX-96 experimental site. The inversion technique is then applied to measured data obtained from a radial track in SWellEX-96. The geometric parameters of the experiment and dominant geoacoustic parameters were successfully recovered for data with relatively low signal-to-noise ratio.

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Section: Swellex-96 Datamentioning

confidence: 99%

Section: Swellex-96 Datamentioning

confidence: 99%

See 1 more Smart Citation

Range-dependent matched-field inversion of SWellEX-96 data using the downhill simplex algorithm

Musil¹,

Chapman²,

Wilmut³

1999

The Journal of the Acoustical Society of America

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Performance analysis of matched field processing localization with various line array configurations based on normal mode decomposition

Jia¹,

Lin²,

Guo³

et al. 2018

Acta Phys. Sin.

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Various line array configurations are evaluated for the source localization performance based on the analysis of mode decomposition matrix in this paper. The guideline of array shape design focuses on improving the localization performance of matched filed processing, meanwhile reducing the difficulty of deploying equipment in practical experiments. In the shallow water environment, when the environment is well known, the source localization result can be obtained by matched field processing algorithms effectively, but the source localization performance is affected by the array parameters, such as array length, the number of sensors, and the configurations of various horizontal and vertical line arrays. The modal decomposition method provides a useful insight into the questions of how many modes are needed and how to design the array to resolve the modes. Therefore, the method of utilizing a normal mode acoustic propagation model to decompose mode is proposed by vertical line array, horizontal line array and combined array respectively. Then we can evaluate the source localization performance of various line array configurations by studying the characteristic of normal mode decomposition matrix, thus establishing a qualitative or even quantitative relationship between each other. The more the normal mode decomposition matrix tends to be diagonalized, the better performance of line array localization will be obtained. Simulation results show that the localization performance of matched field processing with the combined arrays will be severely degraded when the mode amplitudes cannot be accurately deduced by one of the sub-arrays. Considering the requirements for the practical experiments and various environments, the source localization performance of short vertical line array and combined array are mainly discussed in this paper. The combined array can increase the azimuth and depth information of the source and realize three-dimensional target detection while the vertical array provides range-depth information and the horizontal array provides bearing information. Simulation result indicates that the design guidelines based on the normal mode decomposition are appropriate for arrays employed for matched filed processing. Meanwhile, the combined arrays perform better than the short vertical array, which is benefited by the horizontal array's suppressing the side lobes, which leads the ratio of peak to sidelobe to increase, and thus improving the location accuracy. The values of localization accuracy of combined arrays are all above 90% according to the simulation experiment. Take the practical application into account, the combined array is undoubtedly a compromise choice for the localization performance and the test complexity.

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Detection of low-level broadband signals using adaptive matched-field processing with horizontal arrays

Cited by 2 publications

References 0 publications

Range-dependent matched-field inversion of SWellEX-96 data using the downhill simplex algorithm

Range-dependent matched-field inversion of SWellEX-96 data using the downhill simplex algorithm

Performance analysis of matched field processing localization with various line array configurations based on normal mode decomposition

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