SSANet: normal-mode interference spectrum extraction via SSA algorithm-unrolled neural network - Zhu, Shuping; Gao, Wei; Li, Xiaolei (Frontiers in Marine Science, 2024)
In ocean acoustic fields, extracting the normal-mode interference spectrum (NMIS) from the received sound intensity spectrum (SIS) plays an important role in waveguide invariant estimation and underwater source ranging. However, the received SIS often has a low signal-to-noise ratio(SNR) due to the ocean ambient noise and the limitations of received equipment. This can lead to significant performance degradation for the traditional methods of extracting NMIS at low SNR conditions. To address this issue, a new deep neural network model called SSANet is proposed to obtain NMIS based on unrolling the traditional singular spectrum analysis (SSA) algorithm. First, the step of embedding and singular value decomposition (SVD) in the SSA is achieved by the convolutional network. Second, the grouping step of SSA is simulated using the matrix multiply weight layer, ReLU layer, point multiply weight layer and matrix multiply weight layer.Third, the diagonal averaging step is implemented by the fully-connected network. Simulation results in the canonical ocean waveguide environments demonstrate that SSANet outperforms other traditional methods such as Fourier transform (FT), multiple signal classification (MUSIC), and SSA in terms of root mean square error, mean absolute error, and extracting performance.