Autoregressive model for high-resolution wavenumber estimation in a shallow water environment using a broadband...

Area of Science:

• Ocean acoustics
• Signal processing
• Wave phenomena

Background:

• Estimating wavenumbers in shallow water is crucial for understanding wave propagation.
• Traditional methods like spatial Fourier transforms require numerous hydrophones for accurate resolution.
• Wideband frequency-wavenumber estimation presents challenges in sensor deployment.

Purpose of the Study:

• To present and validate an autoregressive (AR) model for high-resolution wavenumber spectrum estimation.
• To determine the minimum number of sensors required for the AR model using a stabilization diagram.
• To demonstrate the effectiveness of the AR model in shallow water environments compared to existing methods.

Main Methods:

• Application of an autoregressive (AR) model to horizontal array measurements.
• Computation of time and spatial Fourier transforms for frequency-wavenumber representation.
• Utilizing a stabilization diagram to identify the minimum sensor array size for AR model stability.

Main Results:

• The AR model accurately estimates wavenumbers across a wide frequency band.
• Fewer hydrophones are needed with the AR model compared to the spatial Fourier Transform method.
• Validation confirmed the method's accuracy on both simulated and experimental data.

Conclusions:

• The AR model offers a more sensor-efficient approach to high-resolution wavenumber estimation in shallow water.
• This method enhances the feasibility of wideband wavenumber analysis with limited sensor arrays.
• The AR model provides a robust alternative for acoustic measurements in challenging shallow water conditions.