Domain-Adaptive HRRP Generation Using Two-Stage Denoising Diffusion Probability Model

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, result 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 pre-generated contents. In the second stage, we harness style information from reference samples to steer the pre-generated content closer to the testing domain. By augmenting training data with the generated samples, the disparity between 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 17° depression angle, respectively.