基于时序-脉幅聚类的未知雷达辐射源信号分选方法

林进健; 黄明军; 孙慧博; 林梓涵; 谢恺

doi:10.12263/DZXB.20250341

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基于时序-脉幅聚类的未知雷达辐射源信号分选方法

学术论文 | 更新时间：2025-12-27

- 基于时序-脉幅聚类的未知雷达辐射源信号分选方法
- A Temporal-Pulse Amplitude Clustering-Based Unknown Radar Emitter Signal Sorting Method
- 电子学报 2025年53卷第9期页码：3256-3273
- 作者机构：
  
  1.中山大学电子与通信工程学院，广东深圳 518107
  2.中国电子科技集团公司第二十九研究所，四川成都 610036
- 作者简介：
  
  [ "林进健男，1993年1月生，广东阳江人. 中山大学博士研究生.主要研究方向为雷达信号处理、不确定信息融合. E-mail: linjj77@mail2.sysu.edu.cn" ]
  [ "黄明军男，1990年10月生，安徽舒城人. 中国电子科技集团公司第二十九研究所高级工程师.主要研究方向为作战支持、雷达信号与信息处理等. E-mail: onion_hmj@163.com" ]
  [ "孙慧博男，2002年4月生，河南新乡人. 中山大学硕士研究生.主要研究方向为电子对抗、雷达信号分选. E-mail: sunhb6@mail2.sysu.edu.cn" ]
  [ "林梓涵男，2003年1月生，安徽合肥人. 中山大学硕士研究生.主要研究方向为电子对抗、雷达信号分选. E-mail: linzh266@mail2.sysu.edu.cn" ]
  [ "谢恺男，1980年3月生，安徽巢湖人. 中山大学教授、博士生导师.主要研究方向为雷达信号处理、电子对抗信号处理等. E-mail: xiek8@mail.sysu.edu.cn" ]
- 基金信息：
  
  科技委基础加强重点基金(2022-JCJQ-JJ-0726);国防预研项目(1002;917;1004);中山大学科学研究重点建设项目(2024_76150_B25899);电磁空间作战与应用重点实验室开放基金(CETCKL20230010)
- DOI：10.12263/DZXB.20250341
  中图分类号： TN957.51;
- 收稿：2025-04-29，
  
  录用：2025-09-09，
  
  纸质出版：2025-09-25
- 稿件说明：
移动端阅览
林进健, 黄明军, 孙慧博, 等. 基于时序-脉幅聚类的未知雷达辐射源信号分选方法[J]. 电子学报, 2025, 53(09): 3256-3273.

LIN Jin-jian, HUANG Ming-jun, SUN Hui-bo, et al. A Temporal-Pulse Amplitude Clustering-Based Unknown Radar Emitter Signal Sorting Method[J]. Acta Electronica Sinica, 2025, 53(09): 3256-3273.
林进健, 黄明军, 孙慧博, 等. 基于时序-脉幅聚类的未知雷达辐射源信号分选方法[J]. 电子学报, 2025, 53(09): 3256-3273. DOI：10.12263/DZXB.20250341

LIN Jin-jian, HUANG Ming-jun, SUN Hui-bo, et al. A Temporal-Pulse Amplitude Clustering-Based Unknown Radar Emitter Signal Sorting Method[J]. Acta Electronica Sinica, 2025, 53(09): 3256-3273. DOI：10.12263/DZXB.20250341

摘要

在复杂电磁环境中，多个未知雷达辐射源发射的脉冲在时域高度交织，其射频、脉宽等参数彼此高度相似，导致常规的分选方法性能下降.相比之下，脉冲幅度（Pulse Amplitude，PA）受雷达天线方向图、波束扫描方式等物理机制影响，特别是在机械扫描型雷达，呈现出可识别的包络变化规律，可为分选提供补充判别信息.基于此，本文提出一种基于到达时间（Time Of Arrival，TOA）与脉冲幅度的雷达信号分选方法.本文首先分析PA在不同雷达工作模式下的时序变化规律，借鉴密度聚类思想，在TOA-PA 2维空间中，结合邻域半径与局部斜率变化约束，识别具有相似几何形态的脉冲子集，生成初始聚类路径组.为解决因漏脉冲或噪声干扰导致的同源轨迹断裂问题，提出了聚类路径融合方法.通过时间重叠率筛选候选路径对，计算全局及局部斜率熵以评估PA趋势一致性，并采用Hausdorff距离度量路径间空间相似性度量，实现相似路径融合，构建了具有物理可解释性的PA包络轨迹.最后，对融合后的TOA序列构建一阶差分直方图，结合关联脉冲对方法完成脉冲重复间隔（Pulse Repetition Interval，PRI）候选分组与参数统计.实验在4种仿真场景下进行，涵盖10%~50%不同组合的漏脉冲率与噪声脉冲率.以纯度、

值、福尔克斯-马洛斯指数和调整兰德系数4项指标评估聚类性能，并与7种主流聚类算法对比.结果表明，所提方法在综合性能上显著优于对照组；路径融合机制有效抑制“增批”问题，提升聚类时序连续性与辐射源对应一致性；PRI估计平均相对误差不超过0.6%.本文研究了基于TOA与PA联合特征的初分选，引入基于时空相似度的路径融合策略，利用TOA一阶差分直方图进行PRI主分选，完成对PRI的检测.该方法适用于非合作电子侦察中未知辐射源的分选问题.后续研究可聚焦于聚类超参数的自适应整定、多节拍分选结果的证据融合机制，以及在实测脉冲描述字（Pulse Descriptive Word，PDW）数

据集上的泛化性能验证.

Abstract

In complex electromagnetic environments

pulses emitted from multiple unknown radar emitters are highly interleaved in the time domain. Conventional deinterleaving methods suffer from performance degradation because key parameters

such as radio frequency (RF) and pulse width (PW)

often exhibit high similarity to one another. In contrast

pulse amplitude (PA) is influenced by underlying physical mechanisms

including the antenna radiation pattern and the beam scanning mode. This is particularly evident in mechanically scanned radars

where PA presents recognizable envelope variation patterns that can provide supplementary discriminative information for deinterleaving. Based on this premise

this paper proposes a radar signal deinterleaving method founded on the joint use of time of arrival (TOA) and pulse amplitude (PA).The proposed method first analyzes the temporal variation patterns of PA under different radar operational modes. Inspired by density-based clustering

it identifies pulse subsets with similar geometric morphologies in the 2D TOA-PA space by combining constraints on neighborhood radius and local slope variations

thereby generating an initial set of cluster paths. To address the fragmentation of co-source tracks caused by missing pulses or noise interference

a cluster path fusion method is introduced. It screens candidate path pairs through temporal overlap

calculates global and local slope entropy to assess PA trend consistency

and employs the Hausdorff distance to measure spatial similarity between paths. This process merges similar paths to reconstruct physically plausible PA envelope tracks. Finally

a first-order difference histogram is constructed from the TOA sequence of the fused tracks

and pulse repetition interval (PRI) candidate grouping and parameter statistics are completed through an associated pulse pair analysis. Experiments are conducted in four simulated scenarios

covering various combinat

ions of missing pulse rates and noise pulse rates from 10% to 50%. Clustering performance was evaluated using four metrics—purity

-score

Fowlkes-Mallows index (FMI)

and adjusted rand index (ARI)—and benchmarked against seven mainstream clustering algorithms. The results demonstrate that the proposed method significantly outperforms the control group in overall performance. The path fusion mechanism effectively suppresses the generation of spurious emitters

enhances the temporal continuity of clusters

and improves their correspondence to true emitters. The average relative error for PRI estimation did not exceed 0.6%. In summary

this paper performs an initial sort using joint TOA-PA features

introduces a path fusion strategy based on spatio-temporal similarity

and conducts the main deinterleaving via a TOA first-order difference histogram to achieve robust PRI detection. The approach is well-suited for deinterleaving unknown emitters in non-cooperative electronic reconnaissance. Future research could focus on the adaptive tuning of clustering hyperparameters

an evidence fusion mechanism for multi-epoch deinterleaving results

and validation of generalization performance on measured

real-world pulse descriptive word (PDW) datasets.

关键词

Keywords

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