面向ADS-B信号辐射源个体识别的轻量化模型设计

针对辐射源个体识别高精确度、轻量化、实时性的现实应用需求，提出了面向广播式自动相关监测(ADS-B)信号辐射源个体识别的轻量化模型设计方法。根据信号数据特点进行解码处理，并对不均衡样本进行权重调节，改善样本质量；通过分组卷积获取不同维度的细微特征，与初始特征拼接，实现多维互补特征融合，并联同步进行提高识别效率。利用Ghost bottleneck结构实现网络模型压缩与跨层连接，在融合多维特征的同时节省计算资源。实验结果表明，本文算法结构精简，计算量低，识别率达到95.2%，并在不同容量的样本识别中效果稳定。本文算法较好地平衡了辐射源个体识别精确度、轻量化与高时效的需求。

Design of lightweight model for Specific Emitter Identification of ADS-B signal

Aiming at the practical application requirements of high precision, lightweight and instant for Specific Emitter Identification(SEI), a lightweight model design for radiation source individual recognition of Automatic Dependent Surveillance-Broadcast(ADS-B) signal is proposed in this paper. Firstly, the signal data is decoded according to the characteristics of the signal data, and the weight of the unbalanced sample is adjusted to improve the sample quality. Then, the small features of different dimensions are obtained by grouping convolution and splicing with the initial features to realize multidimensional complementary feature fusion and parallel synchronization to improve the recognition efficiency. Network model compression and cross-layer connection are implemented by using a Ghost bottleneck structure, which tends to save computing resources while integrating multi-dimensional characteristics. The experimental results show that the proposed algorithm has the advantages of simple structure and low computational load, high recognition rate of 95.2%, and a stable recognition effect in different capacity samples. The proposed design better balances the needs of individual identification accuracy, lightweight and efficiency for SEI.