|
|
|
| HRRP Data Augmentation Based on Conditional Diffusion Model |
| SU Yalin1， JIANG Guotao2，3， ZHANG Tao1， MA Jin1，
WEI Feiming1， YU Wenxian1 |
| 1. School of Automation and Intelligent Sensing， Shanghai Jiao Tong University， Shanghai 200240， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China；
3. National Key Laboratory of Automatic Target Recognition（Shanghai）， Shanghai 201109， China |
|
|
|
|
Abstract This paper introduces a data augmentation technique using a conditional diffusion model to tackle issues related to limited samples and cross-domain distribution shifts in High-Resolution Range Profile (HRRP) data. The proposed approach enhanced the traditional diffusion model by incorporating an angle modulation mechanism that processes azimuth and elevation angles. These angular values were mapped to a high-dimensional continuous space using sine-cosine encoding and then used to modulate category embeddings via a linear transformation, thereby strengthening the model's capacity to capture dependencies between viewing angles and target classes. Additionally, a one-dimensional Fréchet Inception Distance (FID) evaluation metric, leveraging a Temporal Convolutional Network (TCN) for feature extraction, was employed to quantitatively assess the distributional similarity between generated and real HRRP data. Experimental results show that the HRRP data generated by the proposed method achieves a significantly lower one-dimensional FID score than produced by conventional conditional diffusion models. Adding generated samples to the actual training dataset increases the average classification accuracy by 8.55 percent point, demonstrating the effectiveness and practical value of the proposed HRRP data augmentation method.
|
|
Received: 10 November 2025
Published: 11 March 2026
|
|
|
|
|
|
| [1] |
XIA Xiaoyan, ZHANG Yu, HU Xikun, ZHONG Ping. Research on Physical Adversarial Attack Methods for UAV Remote Sensing Target Detection Based on Diffusion Models[J]. Air & Space Defense, 2026, 9(1): 52-62. |
| [2] |
CUI Shan, PAN Junyang, WANG Wei, GUO Ye, XU Jiangtao. Air Defence and Anti-Missile Interception Decision-Making Study Based on Deep Learning[J]. Air & Space Defense, 2024, 7(5): 54-64. |
| [3] |
ZHANG Yanhe, ZANG Yuejin, CHEN Bo, XU Mingsheng. Radar HRRP Target Recognition Algorithm Based on Variational Auto-encoder with Disentangled Representation[J]. Air & Space Defense, 2022, 5(2): 87-93. |
|
|
|
|
2026, Vol. 9 
