@inproceedings{8b6175eb0d124d729cb948e3c8a8085e,
title = "Acceleration method for target HRRP generation based on DDPM",
abstract = "The target high resolution range profile (HRRP) is the sum of the vector projected on the radar line of sight of the target scatters, which can reflect the radial structure characteristics of the target and is one of the important means of radar target recognition. Existing HRRP-based target recognition methods adopt the idea of machine learning classification, which usually require sufficient training samples to obtain satisfactory recognition performance. To solve the dilemma of insufficient radar HRRP data caused by factors such as non-cooperative target and complexity of data acquisition process, this paper uses denoising diffusion probabilistic models (DDPM) to generate HRRP data. Furthermore, to solve the problem that the DDPM generation speed is too slow to meet the actual needs, this paper designs a fast DDPM HRRP (FDH) data generation method based on progressive knowledge distillation. Finally, the experimental results show that the generation speed is increased by about 25 times compared with the initial model under the premise of losing 7.6\% similarity.",
keywords = "Accelerate, DDPM, HRRP, Progressive Knowledge Distillation",
author = "Xitai Shen and Qiang Zhou and Bingqian Yu and Xin Zhang and Yanhua Wang and Liang Zhang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 ; Conference date: 22-11-2024 Through 24-11-2024",
year = "2024",
doi = "10.1109/ICSIDP62679.2024.10868454",
language = "English",
series = "IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024",
address = "United States",
}