The exploitation and utilization of seabed sediments provide vital significance in many field. Recently, the classification of seabed sediments using Sub-Bottom Profiler (SBP) data has become a research focus. Concretely speaking, SBP data can be applied not only for recognizing the individual stratigraphic layers but also for classifying the seabed sediments by inversion models. To improve the sorting effect of gravel and mud simultaneously, we propose a sediment classification method based on the back propagation neural network (BPNN) with the Biot-Stoll model and the Attenuation-Based model. In this method, two datasets of the mean grain size derived from these two models respectively are trained through the BPNN classifier to classify seabed sediments. The proposed method is verified through the SBP data and in-situ sampling data collected from the sea north of Shandong Peninsula, China. The experimental results show that the overall accuracy of sediment classification is 89.4%, and the classification accuracy of gravel and mud reach 91.4% and 93.3%, respectively, confirming that gravel and mud can be more accurately distinguished based on the proposed method than the single Biot-Stoll model and the single Attenuation-Based model.
To estimate the direction of arrival (DOA) of a linear frequency modulation (LFM) signal in a low signal-to-noise ratio (SNR) hydroacoustic environment by a small aperture array, a novel deconvolved beamforming method based on fractional Fourier domain delay-and-sum beamforming (FrFB) was proposed. Fractional Fourier transform (FrFT) was used to convert the received signal into the fractional Fourier domain, and delay-and-sum beamforming was subsequently performed. Noise resistance was acquired by focusing the energy of the LFM signal distributed in the time–frequency domain. Then, according to the convolution structure of the FrFB complex output, the influence of the fractional Fourier domain complex beam pattern was removed by deconvolution, and the target spatial distribution was restored. Therefore, an improved spatial resolution of DOA estimation was obtained without increasing the array aperture. The simulation and experimental results show that, with a small aperture array at low SNR, the proposed method possesses higher spatial resolution than FrFB and frequency-domain deconvolved conventional beamforming.
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