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

Qiang Zhou, Yanhua Wang, Xin Zhang, Liang Zhang*, Teng Long

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

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.

源语言英语
文章编号3504305
页(从-至)1-5
页数5
期刊IEEE Geoscience and Remote Sensing Letters
21
DOI
出版状态已出版 - 2024

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