基于点云分割网络的雷达信号分选方法

The Radar Signal Deinterleaving Method Base on Point Cloud Segmentation Network

针对现有基于图像分割的端到端雷达信号分选方法存在的像素点重叠与处理效率不高的问题,该文提出一种基于点云分割网络的端到端分选方法。首先将雷达脉冲流的脉冲描述字(PDW)映射为点云;之后利用点云分割网络(PointNet++)对该点云中各点依据其所属辐射源进行分割;最后将具有相同标签的点聚类形成脉冲集合,分别提取各脉冲集合所包含的辐射源并形成相应的辐射源描述字。仿真结果表明:所提方法能够有效对未知雷达信号进行分选,在脉冲丢失和虚假脉冲干扰的分选环境下也表现出较强的可靠性与稳定性,并且由于采用具有轻量化特点的模型使得该方法的执行效率更高。

To solve the problems of pixel points overlap and low processing efficiency in existing end-to-end radar signal deinterleaving methods based on image segmentation,an end-to-end sorting method using a point cloud segmentation network is proposed in this paper.Firstly,the Pulse Description Words(PWD)of radar pulse stream are mapped to point clouds.Then,the PointNet++is used to segment each point according to its radiation source.Finally,the points with the same label are clustered to form pulse sets,and the radiation sources within each pulse set are then extracted to form corresponding emitter description words.The simulation results demonstrate that the proposed method can effectively separate unknown radar signals while maintaining reliability and stability,even in scenarios with pulse loss and false pulse interference.Additionally,the implementation efficiency of this method is higher because of the model with lightweight characteristics.