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Electromagnetic sea clutter prediction via hybrid neural networks for covert maritime communication
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DOI：10.23919/jcc.fa.2025-0304.202604.png)
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Abstract
En 中文
The security of maritime communication for transmitting sea clutter signals is of critical importance from both information-theoretic and electromagnetic signal processing perspectives. Existing prediction models face significant challenges in accurately capturing the non-stationary and chaotic characteristics of sea clutter, a typical electromagnetic scattering signal influenced by complex maritime environments. To address this, we propose a novel maritime covert communication scheme based on information-theoretic secrecy metrics, leveraging communication relay unmanned aerial vehicles to minimize the monitor's detection probability. A hybrid neural network model is developed for sea clutter prediction by integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism. Initially, phase space reconstruction is applied to sea clutter signals measured by IPIX radar, exploiting their spatiotemporal correlation—a key property in electromagnetic signal analysis. The CNN extracts spatial features from the reconstructed signals, while the BiLSTM models temporal dependencies, effectively mitigating overfitting in long-sequence prediction. The attention mechanism further enhances performance by dynamically weighting salient features. Experimental results demonstrate superior prediction accuracy and robust target detection capability based on prediction errors. This work bridges deep learning-based electro magnetic signal processing and covert communication design, providing insights for the design of secure maritime information systems.
Keywords:
covert communication
electromagnetic signal processing
hybrid neural network
prediction
sea clutter
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