基于DB-YOLO的双基地雷达弱运动目标检测方法

Bistatic radar weak moving target detection method based on DB-YOLO

摘要:

非合作双基地雷达因其特殊的探测方式，致使回波中目标信噪比较低，特别是海上运动目标，在雷达扫描周期的帧与帧之间探测并不稳定，会对后续目标跟踪造成较大困难。本文首先采用低门限恒虚警率(CFAR)检测器将雷达距离-多普勒维和距离-方位维的检测结果匹配，得到相应掩码图，筛选出潜在的运动目标；然后提出一种融合多维特征信息的双主干YOLO(DB-YOLO)，该网络采用双主干结构，同时提取动目标掩码图和其映射下相同尺度P显图的特征，并采用深度可分离卷积模块降低网络的模型参数。将该模型与Faster RCNN、YOLOv5及其常见变种YOLOv5-ConvNeXt进行对比，实验表明，DB-YOLO有效提高了目标检测性能并保证了推理速度，为非合作双基地雷达的目标跟踪奠定了基础。

Abstract:

Non-cooperative bistatic radar has a low signal-to-noise ratio in the echo due to its special detection method. In particular, the detection between frames in the radar scanning cycle for maritime moving targets is not stable, which will bring great difficulties for subsequent target tracking. The low threshold Constant False Alarm Rate(CFAR) detector is employed to match the detection results of radar range-Doppler dimension and range-azimuth dimension to obtain the corresponding mask map, and the potential moving targets are found. Then, a Double Backbone-YOLO(DB-YOLO) that fuses multi-dimensional feature information is proposed. The network adopts a dual-trunk structure, extracts the features of the moving target mask map and the same-scale P-display map under its mapping, and uses a deep separable convolution module to reduce the model parameters of the network. Finally, the comparison experiments with Faster RCNN, YOLOv5 and its common variant YOLOv5-ConvNeXt show that DB-YOLO effectively improves the target detection performance and ensures the inference speed, which lays a foundation for target tracking of noncooperative bistatic radar.