Traditional clustering algorithms can be applied for the pre-sorting step of radar signal sorting. It can effectively dilute the pulse stream and prevent the dense pulse stream from interfering pulse repetition interval (PRI) extraction. However, the pre-sorting deviation will cause interference and missing pulses during the main sorting process. To solve this problem, we deploy the unmanned aerial vehicle (UAV) swarm to monitor reconnaissance areas and put forward a novel deep transfer learning based signal sorting method. The UAV swarm can collect the pulses from different time and spatial domains, and interference and missing pulses in main sorting processing can be relieved dramatically. In our model, we pre-train our model with the data collected from multiple source areas, which corresponds to different areas detected by different parts of UAV swarms. Then we fine-tune our model with the data of the target area. The experimental results prove that the signal sorting accuracy of methods based on deep transfer learning, i.e., YOLO-MobileNet, F-RCNN and cascade RCNN, are higher than that of the baseline methods. In addition, the signal sorting accuracy of traditional methods based on deep learning can be greatly improved with the help of transfer learning.

传统的聚类算法可以应用于雷达信号排序的预排序步骤。它可以有效地稀释脉冲流，防止密集的脉冲流干扰脉冲重复间隔（PRI）提取。但是，预分选偏差会在主分选过程中造成干扰和漏脉冲。为了解决这个问题，我们部署了无人机（UAV）群来监视侦察区域，并提出了一种新的基于深度迁移学习的信号排序方法。无人机群可以对不同时空域的脉冲进行采集，极大地缓解了主分选过程中的干扰和丢失脉冲。在我们的模型中，我们使用从多个源区域收集的数据对模型进行预训练，对应于无人机群不同部位检测到的不同区域。然后我们用目标区域的数据微调我们的模型。实验结果证明，基于深度迁移学习的方法，即YOLO-MobileNet、F-RCNN和级联RCNN的信号排序精度高于基线方法。此外，在迁移学习的帮助下，基于深度学习的传统方法的信号排序精度可以大大提高。