Match-Mode Autoregressive Method for Moving Source Depth Estimation in Shallow Water Waveguides

Liang Guo-Long, Zhang Yi-Feng, Zou Nan and Wang Jin-Jin

Mathematical Problems in Engineering, 2018, vol. 2018, 1-15

Abstract:

Source depth estimation is always a problem in underwater acoustic area, because depth estimation is a nonlinear problem. Traditional depth estimation methods use a vertical line array, which has disadvantage of poor mobility due to the size of sensor array. In order to estimate source depth with a horizontal line array, we propose a matched-mode depth estimation method based on autoregressive (AR) wavenumber estimation for a moving source in shallow water waveguides. First, we estimate the mode wavenumbers using the improved AR modal wavenumber spectrum. Second, according to the mode wavenumber estimation, we estimate the mode amplitudes by the wavenumber spectrum, which is obtained by generalized Hankel transform. Finally, we estimate source depth estimation by the peak of source depth function wherein the data mode best matches the replica mode that is calculated using a propagation model. Compared with synthetic aperture beamforming, the proposed method exhibits a better performance in source depth estimation under low signal-to-noise ratio or the small range span. The robustness of the proposed method is illustrated by simulating the performance in mismatched environment.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7824671

DOI: 10.1155/2018/7824671

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