Impulse radio ultrawideband (IR-UWB) radar has high range resolution, strong anti-jamming ability, and low power consumption and has been widely used in target detection and recognition. Currently, existing studies always extract artificial features of echo signals, such as time–frequency images, Doppler features, or time-domain features, and then distinguish these features through well-designed deep networks. However, these manual features are difficult to achieve task-invariant and disentangled representations. The target echo received by UWB radar also has amplitude, time-shift, and target-aspect sensitivity problems. To address the above problems, we propose a novel supervised contrastive learning (SupCon) framework to recognize different vehicles. Under label constraints, deep invariant representations are obtained through contrastive learning of echo signals, improving classification accuracy. First, a 1-D deep residual network (ResNet) is designed as the backbone, and the self-attention (SA) layer is added to extract long-range features of echo signals. Second, well-designed data augmentation methods can improve the performance of contrastive learning. Due to the integration of multiple data transformations, the model can learn invariant features by maximizing the mutual information between different signal transformations. Finally, we modify the SupCon loss function. It alleviates the conflict problem of simultaneously shrinking and expanding the distance between the positive samples in the feature space and improves the recognition performance of the model. Ablation experiments on the measured dataset show that the designed components of the method are effective. Comparative experiments on ultrawideband radar public datasets [Air Force Research Laboratory’s (AFRL) high-resolution range profile (HRRP), moving and stationary target acquisition and recognition (MSTAR)] also demonstrate the excellent classification performance of the proposed algorithm.

中文翻译：

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基于IR-UWB雷达的车辆分类监督对比学习

脉冲无线电超宽带（IR-UWB）雷达具有距离分辨率高、抗干扰能力强、功耗低等特点，已广泛应用于目标检测和识别。目前，现有研究总是提取回波信号的人工特征，如时频图像、多普勒特征或时域特征，然后通过精心设计的深度网络区分这些特征。然而，这些手动特征很难实现任务不变和解开的表示。UWB雷达接收到的目标回波也存在幅度、时移和目标方位灵敏度问题。为了解决上述问题，我们提出了一种新颖的监督对比学习（SupCon）框架来识别不同的车辆。在标签约束下，通过回波信号的对比学习获得深度不变的表示，提高分类精度。首先，设计一维深度残差网络（ResNet）作为主干，并添加自注意力（SA）层来提取回波信号的远程特征。其次，精心设计的数据增强方法可以提高对比学习的性能。由于集成了多个数据变换，模型可以通过最大化不同信号变换之间的互信息来学习不变特征。最后，我们修改了 SupCon 损失函数。它缓解了特征空间中正样本之间的距离同时缩小和扩大的冲突问题，提高了模型的识别性能。对测量数据集的消融实验表明，该方法的设计组件是有效的。超宽带雷达公共数据集[空军研究实验室 (AFRL) 高分辨率距离剖面 (HRRP)、移动和静止目标获取和识别 (MSTAR)] 的比较实验也证明了所提出算法的出色分类性能。