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, 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.

I. Introduction

High resolution range profile (HRRP) represents the distribution of the target scattering centers along the radar line-of-sight (LOS), providing geometric and structural information about the target. In addition, its low demands on data acquisition, storage, and processing make it beneficial for real-time processing [1], [2], [3]. Therefore, it plays a crucial role in radar target recognition.