Radar Target Recognition Method for HRRP Based on Geodesic Flow Kernel

YANG Wei; LI Wei-jie; LIU Yong-xiang; LI Xiang

doi:10.12263/DZXB.20211574

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Radar Target Recognition Method for HRRP Based on Geodesic Flow Kernel

PAPERS | 更新时间：2025-12-08

- Radar Target Recognition Method for HRRP Based on Geodesic Flow Kernel
- ACTA ELECTRONICA SINICA Vol. 51, Issue 3, Pages: 527-536(2023)
- 作者机构：
  
  国防科技大学电子科学学院，湖南长沙 410073
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61871384;61401486;61921001);National Science Foundation of Hunan Province(2017JJ3367)
- DOI：10.12263/DZXB.20211574
  CLC： TN957.51
- Received：24 November 2021，
  
  Revised：2022-07-15，
  
  Published：25 March 2023
- 稿件说明：
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杨威,李玮杰,刘永祥等.基于测地线流式核的雷达目标高分辨距离像鲁棒识别方法[J].电子学报,2023,51(03):527-536.

YANG Wei,LI Wei-jie,LIU Yong-xiang,et al.Radar Target Recognition Method for HRRP Based on Geodesic Flow Kernel[J].ACTA ELECTRONICA SINICA,2023,51(03):527-536.
杨威,李玮杰,刘永祥等.基于测地线流式核的雷达目标高分辨距离像鲁棒识别方法[J].电子学报,2023,51(03):527-536. DOI： 10.12263/DZXB.20211574.

YANG Wei,LI Wei-jie,LIU Yong-xiang,et al.Radar Target Recognition Method for HRRP Based on Geodesic Flow Kernel[J].ACTA ELECTRONICA SINICA,2023,51(03):527-536. DOI： 10.12263/DZXB.20211574.

摘要

非合作目标识别常常面临少量不完备的训练样本、训练样本与测试样本信噪比不一致等现象，本文为此提出了一种基于测地线流式核的雷达目标高分辨距离像鲁棒识别方法.该方法沿格拉斯曼流形中测地线积分提取不变特征，且通过核函数映射可获得解析特征提取表达式.该方法还可作为预处理手段对数据降噪，进一步提高其他算法的识别准确率.实验结果表明，对于信噪比失配和少量不完备样本等问题，该方法都具有鲁棒目标识别能力，并且满足实时性要求.

Abstract

Non-cooperative target recognition often faces a small number of incomplete training samples

and inconsistent signal-to-noise ratio (SNR) between training samples and test samples. A robust radar target recognition method for high resolution range profile (HRRP) based on geodesic flow kernel is proposed in this paper. It extracts invariant features along the geodesic integral in the Glassman manifold

and has an analytical expression through kernel function mapping. The method also can be used as a preprocessing tool to reduce data noise and improve the recognition performance of other algorithms. Experimental results show that the proposed method has robust recognition ability for SNR mismatch and a small number of incomplete samples

and meets the requirements of real-time.

关键词

Keywords

references

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