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CHEN Jian, YU Gang, DU Lan, et al. Few-Shot radar high-resolution range profile: target recognition with simulated data assistance[J]. Journal of Radars, in press. doi: 10.12000/JR25123
Citation: CHEN Jian, YU Gang, DU Lan, et al. Few-Shot radar high-resolution range profile: target recognition with simulated data assistance[J]. Journal of Radars, in press. doi: 10.12000/JR25123
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Few-Shot Radar High-Resolution Range Profile: Target Recognition with Simulated Data Assistance

DOI: 10.12000/JR25123 CSTR: 32380.14.JR25123

  • National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds:  Joint Fund of the Ministry of Education of China (8091B03032401), The National Natural Science Foundation of China (U24B20137, U21B2039), The Aviation Science Foundation(20230020081006)
More Information
  • Corresponding author: CHEN Jian, jianc@xidian.edu.cn
  • Received Date: 2025-07-15
    Available Online: 2026-01-22
    Abstract
  • Research on target recognition using radar high-resolution range profiles (HRRPs) is extensive and diverse in methodology. In particular, the application and development of deep learning to radar HRRP target recognition have enabled efficient, precise target perception directly from radar echoes. However, deep learning-based recognition networks rely on large amounts of training data. For non-cooperative targets, due to limited radar system parameters and rapid target attitude variations, acquiring adequate HRRP training samples that comprehensively cover target attitudes in advance is difficult in practice. Consequently, deep recognition networks are prone to overfitting and exhibit considerably degraded generalization capability. To address these issues, and given the ease of obtaining full-attitude electromagnetic simulation data for the target, this paper leverages simulated data as auxiliary information to mitigate the small-sample-size problem through data augmentation and cross-domain knowledge-transfer learning. For data augmentation, based on the analysis of differences in mean and variance between simulated and measured HRRPs within a given attitude-angle range, a linear transformation is applied to a set of simulated HRRPs spanning the same angular domain as a small set of measured HRRPs. This adjustment ensures that the simulated data’s mean and variance match the characteristics of the measured HRRPs, thereby achieving data augmentation that approximates the true distributional properties of HRRPs. Meanwhile, for cross-domain knowledge transfer learning, the proposed method introduces a domain alignment strategy based on generative adversarial constraints and a class alignment strategy based on contrastive learning constraints. These approaches draw the domain features of full-attitude simulation—strong discriminability and generalizability—closer to the measured domain features on a class-by-class basis, thereby further aiding learning from the measured domain data and leading to substantial improvements in few-shot recognition performance. Experimental results based on electromagnetic simulated and measured HRRP data for three and ten types of aircraft and ground vehicle targets, respectively, demonstrate that the proposed method yields superior recognition robustness compared with existing few-shot recognition methods.

     

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