Joint time-frequency domain equalization of MSK signal over underwater acoustic channel

Han, Ru; Jia, Ning; Huang, Jianchun; Guo, Shengming

doi:10.1016/j.apacoust.2021.108597

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Signal physics-based methods rely on basic characteristics, temporal features, and non-Gaussian characteristics of underwater acoustic signals (Yao X. et al, 2023). This includes time-domain features like zero-crossing distribution, frequency-domain features like cepstral analysis (Zhu et al, 2022), and joint time-frequency domain features such as wavelet transforms (Han et al, 2022;Tian et al, 2023). Brain-like computing features for underwater acoustic signals include Mel-frequency cepstral coefficients (MFCC) simulating nonlinear processing of the human ear (Di et al, 2023) and Gammatone filtering simulating peripheral auditory processing (Zhou et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

MGFGNet: an automatic underwater acoustic target recognition method based on the multi-gradient flow global feature enhancement network

Chen,

Tang,

Qiu

et al. 2023

Front. Mar. Sci.

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The recognition of underwater acoustic targets plays a crucial role in marine vessel monitoring. However, traditional underwater target recognition models suffer from limitations, including low recognition accuracy and slow prediction speed. To address these challenges, this article introduces a novel approach called the Multi-Gradient Flow Global Feature Enhancement Network (MGFGNet) for automatic recognition of underwater acoustic targets. Firstly, a new spectrogram feature fusion scheme is presented, effectively capturing both the physical and brain-inspired features of the acoustic signal. This fusion technique enhances the representation of underwater acoustic data, resulting in more accurate recognition results. Moreover, MGFGNet utilizes the multi-gradient flow network and incorporates a multi-dimensional feature enhancement technique to achieve fast and precise end-to-end recognition. Finally, a loss function is introduced to mitigate the influence of unbalanced data sets on model recognition performance using Taylor series. This further enhances model recognition performance. Experimental evaluations were conducted on the DeepShip dataset to assess the performance of our proposed method. The results demonstrate the superiority of MGFGNet, achieving a recognition rate of 99.1%, which significantly surpasses conventional methods. Furthermore, MGFGNet exhibits improved efficiency compared to the widely used ResNet18 model, reducing the parameter count by 51.28% and enhancing prediction speed by 33.9%. Additionally, we evaluated the generalization capability of our model using the ShipsEar dataset, where MGFGNet achieves a recognition rate of 99.5%, indicating its superior performance when applied to unbalanced data. The promising results obtained in this study highlight the potential of MGFGNet in practical applications.

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