High-resolution range profile (HRRP) plays a crucial role in radar target recognition. In real-world applications, variations in operational conditions during testing, such as changes in depression angles, resulting in unsatisfactory performance for HRRP target recognition methods. One way to alleviate this issue is to augment training data with samples that embody the testing domain style. Therefore, we propose a domain-adaptive HRRP generation approach based on a two-stage denoising diffusion probability model (DDPM). In the first stage, we leverage the category labels as conditioning factors, ensuring precise category control over the pregenerated contents. In the second stage, we harness style information from reference samples to steer the pregenerated content closer to the testing domain. By augmenting training data with the generated samples, the disparity between the two domains is bridged. Results on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method improves the recognition rate by 2.01% and 4.93% for the data of 15° and 30° depression angle, respectively.

中文翻译：

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使用两阶段去噪扩散概率模型生成域自适应 HRRP

高分辨率距离剖面（HRRP）在雷达目标识别中起着至关重要的作用。在实际应用中，测试过程中操作条件的变化，例如俯角的变化，导致 HRRP 目标识别方法的性能不理想。缓解此问题的一种方法是使用体现测试领域风格的样本来增强训练数据。因此，我们提出了一种基于两阶段去噪扩散概率模型（DDPM）的域自适应 HRRP 生成方法。在第一阶段，我们利用类别标签作为条件因素，确保对预生成内容的精确类别控制。在第二阶段，我们利用参考样本中的样式信息来引导预生成的内容更接近测试领域。通过使用生成的样本来增强训练数据，可以弥合两个领域之间的差异。在运动和静止目标获取与识别（MSTAR）数据集上的结果表明，该方法对于15°和30°俯角数据的识别率分别提高了2.01%和4.93%。