Source Depth Classification Using Horizontal Linear Array and Synthetic Aperture Beamforming

A single hydrophone using synthetic aperture beamforming can estimate horizontal modal wavenumbers, and hence providing a potential for classifying surface and submerged sources. Unfortunately, the source depth classification performance degrades rapidly when the input signal-to-noise ratio (SNR) is low. In this paper, to solve the problem, we proposed a source depth classification method, which applies the synthetic aperture beamforming to an HLA. Firstly, the HLA associated with the array beamforming is adopted to process the acoustic signal radiated by a moving source. Secondly, the synthetic aperture beamforming is used to process the beamformer output to obtain the horizontal modal wavenumber spectra. Lastly, the source-depth ambiguity function is obtained by matching the horizontal modal wavenumber spectra with the (previously) calculated mode depth functions. The calculated mode depth functions are based on the nominal sound speed profile, thereby ensuring the robustness of the proposed method. The HLA has a higher array gain than a single hydrophone. Hence, the HLA and synthetic aperture beamforming can effectively improve the quality of the estimated modal wavenumbers, and hence, ensuring a better source depth classification performance. Numerical simulations show that the proposed method can significantly improve the source classification performance in low SNR environments.