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Jamming recognition method based on wavelet packet decomposition and improved deep learning
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DOI：10.1016/j.dcan.2025.05.004.png)
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Abstract
En 中文
To overcome the challenges of poor real-time performance, limited scalability, and low intelligence in conventional jamming pattern recognition methods, this paper proposes a method based on Wavelet Packet Decomposition (WPD) and enhanced deep learning techniques. In the proposed method, an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall (SW), which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network. The network employs a bilateral filter to preprocess the input SW, thereby enhancing the edge features of the jamming signals. To extract abstract features, depthwise separable convolution is utilized instead of traditional convolution, thereby reducing the network's parameter count and enhancing real-time performance. A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes, thus enhancing scalability. During network training, adaptive moment estimation is employed as the optimizer, allowing the network to dynamically adjust the learning rate and accelerate convergence. A comprehensive comparison between the proposed jamming recognition network and six other models is conducted, along with Ablation Experiments (AE) based on numerical simulations. Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy, network complexity, and prediction time.
Keywords:
Wavelet packet decomposition
Improved deep learning
Spectrogram waterfall
Pyramid pooling
Jamming recognition
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