距离-多普勒图的超分辨率：基于线性调频序列雷达与Transformer的案例研究
Super-Resolution of Range-Doppler Maps: A Case Study with Chirp-Sequence Radar and Transformer
浏览论文内容
中文总结 AI 辅助
本研究针对线性调频序列雷达生成的距离-多普勒图分辨率受限问题，提出基于双路径移位窗口注意力的内存高效Transformer架构，结合CFAR损失优化目标可检测性，实现真实环境下高效超分辨率。
中文摘要 AI 辅助
由线性调频序列（Chirp-Sequence，CS）雷达系统生成的距离-多普勒（Range-Doppler，rD）图，其分辨率根本上受限于带宽、载波频率和相干处理间隔的约束。由于监管要求、成本及实时运行需求，通过硬件提升分辨率往往不切实际。本研究使用英飞凌（Infineon）毫米波CS雷达采集的真实数据集，探究基于深度学习的rD图在距离和多普勒维度的超分辨率方法。我们提出一种基于双路径移位窗口（Dual-Path Shifted Window，DPSWIN）注意力的内存高效Transformer架构，结合轴向/双路径注意力与1维移位窗口（Shifted Window，SWIN）注意力，以实现高维雷达数据的可扩展处理。将该模型与现有基于2维SWIN注意力的超分辨率模型进行基准测试，我们对这些模型进行适配以适用于rD图超分辨率任务。我们优先考虑重建保真度而非感知质量，采用基于均方根误差（Root Mean Squared Error，RMSE）的训练目标，避免使用可能引入视觉上合理但物理结构错误的对抗性或感知损失。此外，我们纳入基于恒虚警率（Constant False Alarm Rate，CFAR）的目标检测损失，以优化超分辨率后rD图的下游目标可检测性。我们进一步研究信号处理与训练设计选择对超分辨率的影响，包括幅度压缩、空间上采样策略及损失函数的选择。实验结果表明，所提出的框架能在先前未见过的真实环境中实现计算高效的rD图超分辨率。
英文摘要
Range-Doppler (rD) maps produced by chirp- sequence (CS) radar systems are fundamentally limited in reso- lution by bandwidth, carrier frequency, and coherent processing interval constraints. Improving resolution through hardware is often impractical due to regulatory, cost, and real-time operation requirements. In this work, we investigate deep learning-based super- resolution of rD maps in both range and Doppler using a real- world dataset collected with an Infineon millimeter-wave CS radar. We propose a memory-efficient transformer architecture based on dual-path shifted window (DPSWIN) attention, which combines axial/dual-path attention with 1D shifted window (SWIN) attention for scalable processing of high-dimensional radar data. Our model is benchmarked against existing 2D SWIN attention-based super-resolution models, which we adapt to the rD-map super-resolution task. We prioritize reconstruction fidelity over perceptual quality and employ root mean squared error (RMSE)-based training objectives. We avoid adversarial or perceptual losses that may introduce visually plausible but physically incorrect structures. In addition, we incorporate CFAR-based target-detection losses to optimize downstream target detectability in the super-resolved rD maps. We further study the impact of signal processing and training design choices on the super-resolution, including magnitude compression, spatial upsampling strategies, and loss formulations. Experimental results demonstrate that the proposed framework achieves computationally efficient rD map super-resolution on previously unseen real-world environments.
