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            <idno type="halRefHtml">&lt;i&gt;Applied Acoustics&lt;/i&gt;, 2023, 211, pp.109513. &lt;a target="_blank" href="https://dx.doi.org/10.1016/j.apacoust.2023.109513"&gt;&amp;#x27E8;10.1016/j.apacoust.2023.109513&amp;#x27E9;&lt;/a&gt;</idno>
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                <term xml:lang="en">Low signal-to-noise ratio</term>
                <term xml:lang="en">Intensity fluctuation</term>
                <term xml:lang="en">Deep learning</term>
                <term xml:lang="en">Active signal detection</term>
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              <p>To achieve efficient underwater acoustic (UWA) signal detection in low signal-to-noise ratio (SNR) scenarios, the transmitted signals are designed with a large time-bandwidth product to get a high detectiongain. The linear correlator (LC) is considered as the maximum SNR detector, whose detection gain is proportional to the time-bandwidth product. However, the detection performance of LC degrades significantly in time-variant multipath UWA channel and non-Gaussian UWA ambient noise. In this study,we present a deep-learning based two-stage UWA signal detection method in intensity fluctuation environments. This method takes the advantages of the Conv-TasNet and Encoder-Decoder network, whichutilizes an encoder module to extract signal features, a separation module to enhance the signal components and then another decoder module to reconstruct the transmitted signal. To demonstrate the performance of the proposed method, the datasets used for training and testing originated from theASIAEX 2001 South China Sea (SCS) experiment. The experimental results show that our model outperforms the classical LC and channel estimation based LC (CE-LC) in constant false alarm rate (CFAR) detection and also surpasses the TCDAE and Conv-Tasnet models as evaluated by DSI-SNR1 and DSI-SNR2.</p>
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