High-Range Resolution Radar Target Recognition Based on Deep Interpretable Probabilistic Auto-Encoder Model

LIAO Lei-yao

doi:10.12263/DZXB.20240024

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High-Range Resolution Radar Target Recognition Based on Deep Interpretable Probabilistic Auto-Encoder Model

PAPERS | 更新时间：2025-12-11

- High-Range Resolution Radar Target Recognition Based on Deep Interpretable Probabilistic Auto-Encoder Model
- ACTA ELECTRONICA SINICA Vol. 52, Issue 11, Pages: 3847-3857(2024)
- 作者机构：
  
  1.南京邮电大学通信与信息工程学院，江苏南京 210003
  2.西安电子科技大学雷达信号处理全国重点实验室，陕西西安 710071
- 作者简介：
- 基金信息：
  
  Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(NY223135)
- DOI：10.12263/DZXB.20240024
  CLC： TN959.1+7;
- Received：04 January 2024，
  
  Revised：2024-04-09，
  
  Published：25 November 2024
- 稿件说明：
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廖磊瑶. 基于物理可解释自编码模型的雷达目标方位稳健识别算法[J]. 电子学报, 2024, 52(11): 3847-3857.

LIAO Lei-yao. High-Range Resolution Radar Target Recognition Based on Deep Interpretable Probabilistic Auto-Encoder Model[J]. Acta Electronica Sinica, 2024, 52(11): 3847-3857.
廖磊瑶. 基于物理可解释自编码模型的雷达目标方位稳健识别算法[J]. 电子学报, 2024, 52(11): 3847-3857. DOI：10.12263/DZXB.20240024

LIAO Lei-yao. High-Range Resolution Radar Target Recognition Based on Deep Interpretable Probabilistic Auto-Encoder Model[J]. Acta Electronica Sinica, 2024, 52(11): 3847-3857. DOI：10.12263/DZXB.20240024

摘要

现有基于深度神经网络的高距离分辨（High Range Resolution，HRR）雷达目标识别方法是纯数据驱动模型，是1个飞行事故记录器结构，特征不具可解释性，在方位缺失情况下特征泛化性差，模型识别率急剧下降.对此，本文设计了一种物理可解释自编码模型（Physical Interpretable Auto-Encoder Model，PIAEM），解码网络结合雷达目标的散射点模型，利用编码网络挖掘雷达数据具有明确物理含义的散射中心特征，从成像角度描述目标的物理结构特性，如尺寸、轮廓等，在方位缺失情况下具有稳健的物理特性.设计了基于最小重构误差的分类准则，实现雷达目标识别.基于实测高距离分辨雷达回波数据的实验结果表明，本文方法提取的特征具有明确物理含义，且在方位缺失4/5的情况下，PIAEM比现有基于传统目标识别方法的准确率提升了10.27%，验证了本文方法具有方位稳健识别性能.

Abstract

Existing neural network-based high range resolution (HRR) radar target recognition methods are data-driven models that are of black-box structure

which makes it hard to interpret or assess the hidden representations of data. In the case of incomplete target-aspect

neural network-based methods are faced with the issues of poor feature generalization ability and rapid degradation of recognition performance. To access the issues

this paper develops a physical interpretable auto-encoder model (PIAEM). In detail

by incorporating the scattering center model of radar targets into networks

the PIAEM is a physical interpretable model that learns scattering center features with physical meanings. Specially

since the scattering center features reflect the target structure based on radar imaging theory

they are robust under the case of incomplete target-aspect. Moreover

this paper designs a recognition scheme to predict the category of test samples based on the minimum reconstruction error criterion. The experiments on the measured HRR radar dataset validate the effectiveness of our model on learning interpretable features and robust recognition performance

and our PIAEM improves 10.27% rates comparing with traditional radar target recognition methods.

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references

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