Nowadays radar systems have been facing with the disordered electromagnetic spectrum access and utilization in shared spectrum environments with radio communication systems. Numerous waveform recognition methods have been studied with feature engineering and conventional machine learning (ML) for intelligent radar systems, but they are critically challenged by practical problems of scalability and reliability. Deep learning (DL) with the ability to automatically learn the representational features is leveraged to handle the aforementioned obstacles effectively. In this work, we proposed a high-accurate waveform recognition method for intelligent radar systems by developing a novel residual-attention multiscale-accumulation convolutional network (RamNet). By deliberately incorporating the residual connection and attention connection in selective-feature improvement blocks, RamNet can enrich high-impact features without vanishing gradient. Moreover, a structural multiscale-accumulation connection is deployed to improve feature utilization by gathering the high-relevant features at multiple signal resolutions. Experimental results from exhaustive simulation demonstrate that RamNet recognizes waveform robustly under rigorous channel impairments and presents superior performance compared to traditional ML and state-of-the-art DL models.

中文翻译：

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适用于智能雷达系统的基于 CNN 的准确波形识别


如今，雷达系统一直面临着与无线电通信系统共享频谱环境中电磁频谱接入和利用无序的问题。人们已经通过特征工程和传统机器学习（ML）对智能雷达系统研究了许多波形识别方法，但它们受到可扩展性和可靠性的实际问题的严峻挑战。深度学习（DL）具有自动学习表征特征的能力，可有效解决上述障碍。在这项工作中，我们通过开发一种新型的残差注意力多尺度累积卷积网络（RamNet），提出了一种用于智能雷达系统的高精度波形识别方法。通过在选择性特征改进块中有意结合残差连接和注意力连接，RamNet 可以在不消失梯度的情况下丰富高影响力的特征。此外，部署了结构性多尺度累积连接，通过收集多个信号分辨率下的高相关特征来提高特征利用率。详尽仿真的实验结果表明，RamNet 在严格的通道损伤下能够稳健地识别波形，并且与传统的 ML 和最先进的 DL 模型相比，具有卓越的性能。