Abstract
Passive source localization is a challenging task for one receiver, and the pressure sensor provides relatively simple information. An ocean-bottom seismometer (OBS) sensor placed on the seafloor surface can provide more information—not only pressure information, but also three-axis (x-, y-, and z-axis) velocity information at the seafloor interface. In this paper, an OBS sensor was used to estimate the position of the broadband sound source in a Pekeris shallow water waveguide with elastic bottom. As the dynamics that characterize ocean acoustic applications are inherently nonlinear, non-Gaussian, and non-stationary processes that quickly vary with space and time, sequential Bayesian filtering, such as particle filtering (PF), is able to adapt to these environmental changes. Simulation results show that the PF method with the vertical wave impedance (the ratio of the pressure and vertical particle velocity) in the frequency domain as a measurement vector is not affected by source depth and source spectrum information, making it more tolerant and more robust than that with pressure in positioning. Experimental data results verified the effectiveness of the PF method with the vertical wave impedance for the localization of the explosive source.
Highlights
Localization of an underwater sound source is an important practical problem in underwater acoustics
An ocean-bottom seismometer (OBS) sensor was used to estimate the position of the broadband sound source in a Pekeris shallow water waveguide with elastic bottom
In the semi-infinite elastic seabed environment, the expression of pressure and vertical velocity channels received by the OBS sensor were theoretically derived
Summary
Localization of an underwater sound source is an important practical problem in underwater acoustics. Sequential filtering provides a suitable framework for estimating and updating unknown parameters of a system as data become available. Coupling a nonlinear optimization algorithm with the extended Kalman filter (EKF)-based ocean acoustic model can solve the source localization problem in a complex ocean environment. This approach is capable of solving the mismatch problem to some extent. Provided a model-based Bayesian processor to estimate the bearings of moving sources using. Candy et al [4] provided a model-based Bayesian processor to estimate the bearings of moving sources using horizontally towed array data. We derive the vertical wave impedance which the ocean-bottom seismometer bottom seismometer (OBS) sensor obtained in Pekeris waveguide with an elastic bottom.
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