基于改进UNet3+网络的雷达辐射源信号识别

针对传统识别辐射源信号的方法需要手动提取并选取特征、在低信噪比条件下难以准确识别信号的问题，提出了一种基于改进UNet3+网络的辐射源信号识别方法。通过删减UNet3+的网络层级，保留网络特征融合能力的同时降低了网络的复杂度，并引入注意力机制优化模型性能，构建了一个新的网络模型。通过对8种常见的雷达信号进行仿真实验，实验结果表明：改进模型的识别准确率达到96.63%，对比一些经典网络模型，训练总用时更短，在低信噪比条件下能更加有效识别辐射源信号, 可以适应复杂的电磁环境。

A Radar Emitter Signal Recognition Based on Improved UNet3+ Network

Aimed at the problems that traditional emitter signal identification methods often need to carry out artificial feature extraction and signals are difficult to be identified accurately under condition of low SNR environments, a method of emitter signal recognition based on improved UNet3+ network is proposed. By trimming the UNet3+ network hierarchy, the feature fusion ability is retained while the complexity of the network is reduced. The attention mechanism is introduced to optimize the model performance, and a new network model is constructed. The simulation results of eight common radar signals show that the recognition accuracy of the improved model reaches 96.63%. Compared with some classical network models, the total training time is shorter, and the ability to identify the radiation source signal is more effectively under condition of low SNR environments. And the proposed model can also be adapted to the complex electromagnetic environments.