Sparse broadband time varying acoustic response modeling and estimation from an undersampled vertical array with...

Area of Science:

• Underwater acoustics
• Signal processing
• Array signal processing

Background:

• Estimating acoustic response functions in horizontally stratified environments is challenging due to multipath propagation.
• Undersampled vertical arrays offer a potential solution for efficient data acquisition.

Purpose of the Study:

• To develop and validate a method for estimating sparse broadband time-varying acoustic response functions using undersampled vertical arrays.
• To improve coherent multipath combining at the receiver through enhanced sparsity.

Main Methods:

• Utilizing a two-component Gaussian mixture model to capture the sparsity of acoustic arrivals.
• Employing a time-varying increment operator to further sparsify the acoustic response.
• Applying the model to broadband acoustic observations in Buzzard's Bay, MA, using a three-element vertical array.

Main Results:

• The method effectively attenuates non-coherent arrivals while preserving coherent ones.
• Achieved bit error rates below 10^-5 for M-ary orthogonal spread spectrum transmissions at 95 bps over 2 km.
• Demonstrated successful application in shallow water environments with a receive signal-to-noise ratio of -15.5 dB.

Conclusions:

• Sparse estimation techniques using Gaussian mixture models are effective for acoustic channel characterization.
• Undersampled vertical arrays can achieve high-performance underwater acoustic communication.
• The developed method enhances signal robustness and data rates in challenging acoustic environments.