A Temporal-Pulse Amplitude Clustering-Based Unknown Radar Emitter Signal Sorting Method

LIN Jin-jian; HUANG Ming-jun; SUN Hui-bo; LIN Zi-han; XIE Kai

doi:10.12263/DZXB.20250341

您当前的位置：

首页 >

文章列表页 >

A Temporal-Pulse Amplitude Clustering-Based Unknown Radar Emitter Signal Sorting Method

PAPERS | 更新时间：2025-12-27

- A Temporal-Pulse Amplitude Clustering-Based Unknown Radar Emitter Signal Sorting Method
- ACTA ELECTRONICA SINICA Vol. 53, Issue 9, Pages: 3256-3273(2025)
- 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：
- 基金信息：
  
  Key Foundation Project of the Science and Technology Commission for Strengthening Basic Research(2022-JCJQ-JJ-0726);National Defense Pre-research Project(1002;917;1004);Key Scientific Research Construction Project of Sun Yat-sen University(2024_76150_B25899);Open Fund of the Key Laboratory of Electromagnetic Space Operations and Applications(CETCKL20230010)
- DOI：10.12263/DZXB.20250341
  CLC： TN957.51;
- Received：29 April 2025，
  
  Accepted：09 September 2025，
  
  Published：25 September 2025
- 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

references

GUO Q , HUANG S , LIU L C , et al . A radar signal deinterleaving method based on enhanced sparse subspace clustering [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2025 , 61 ( 2 ): 2956 - 2972 .

MOTTIER M , CHARDON G , PASCAL F . Deinterleaving RADAR emitters with optimal transport distances [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2024 , 60 ( 3 ): 3639 - 3651 .

ROGERS J A V . ESM processor system for high pulse density radar environment [J ] . IEE Proceedings F (Communications, Radar and Signal Processing) , 1985 , 132 ( 7 ): 621 - 625 .

MARDIA H K . New techniques for the deinterleaving of repetitive sequences [J ] . IEE Proceedings F (Radar and Signal Processing) , 1989 , 136 ( 4 ): 149 .

MILOJEVIC˜ D J , POPOVIC˜ B M . Improved algorithm for the deinterleaving of radar pulses [J ] . IEE Proceedings F (Radar and Signal Processing) , 1992 , 139 ( 1 ): 98 .

NISHIGUCHI K , KOBAYASHI M . Improved algorithm for estimating pulse repetition intervals [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2000 , 36 ( 2 ): 407 - 421 .

赵仁健 , 熊平 , 倪明 . 大脉冲重复周期调幅信号的压缩平面变换技术 [J ] . 四川大学学报(自然科学版) , 1997 , 34 ( 2 ): 172 - 176 .

ZHAO R J , XIONG P , NI M . The compression plane transformation technique of amplitude modulation signal with large pulse repeat period [J ] . Journal of Sichuan University (Natural Science Edition) , 1997 , 34 ( 2 ): 172 - 176 . (in Chinese)

ZHANG C J , LIU Y C , SI W J . Synthetic algorithm for deinterleaving radar signals in a complex environment [J ] . IET Radar , Sonar & Navigation, 2020 , 14 ( 12 ): 1918 - 1928 .

谢敏 , 赵闯 , 胡德秀 , 等 . 联合DTOA一阶差分曲线和相关分析的参差信号分选方法 [J ] . 电子学报 , 2023 , 51 ( 11 ): 3271 - 3281 .

XIE M , ZHAO C , HU D X , et al . Signal sorting method for jagger PRI radar based on first-order difference curve of DTOA and correlation analysis [J ] . Acta Electronica Sinica , 2023 , 51 ( 11 ): 3271 - 3281 . (in Chinese)

CHENG W H , ZHANG Q Y , DONG J M , et al . An enhanced algorithm for deinterleaving mixed radar signals [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2021 , 57 ( 6 ): 3927 - 3940 .

XIE M , HUANG J , ZHAO C , et al . Radar signal deinterleaving based on hidden Markov chains and residual fence networks [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2025 , 61 ( 3 ): 6011 - 6025 .

WANG C , SUN L T , LIU Z M , et al . A radar signal deinterleaving method based on semantic segmentation with neural network [J ] . IEEE Transactions on Signal Processing , 2022 , 70 : 5806 - 5821 .

隋金坪 , 刘振 , 刘丽 , 等 . 雷达辐射源信号分选研究进展 [J ] . 雷达学报 , 2022 , 11 ( 3 ): 418 - 433 .

SUI J P , LIU Z , LIU L , et al . Progress in radar emitter signal deinterleaving [J ] . Journal of Radars , 2022 , 11 ( 3 ): 418 - 433 . (in Chinese)

QI L G , CHEN H Z , GUO Q , et al . GLS: A hybrid deep learning model for radar emitter signal sorting [J ] . Digital Signal Processing , 2025 , 161 : 105117 .

PINSOLLE J , GOUDET O , ENDERLI C , et al . Deinterleaving of discrete renewal process mixtures with application to electronic support measures [J ] . IEEE Transactions on Signal Processing , 2024 , 72 : 4983 - 4991 .

CHEN T , QIU B C , LI J X , et al . An end-to-end radar pulse deinterleaving structure based on point cloud mapping [J ] . Digital Signal Processing , 2024 , 155 : 104773 .

DUTT R , BALORIA A , PRASAD V R C , et al . Discrete wavelet transform based unsupervised underdetermined blind source separation methodology for radar pulse deinterleaving using antenna scan pattern [J ] . IET Radar , Sonar & Navigation, 2019 , 13 ( 8 ): 1350 - 1358 .

AYAZGOK S , ERDEM C , OZTURK M T , et al . Automatic antenna scan type classification for next-generation electronic warfare receivers [J ] . IET Radar , Sonar & Navigation, 2018 , 12 ( 4 ): 466 - 474 .

YANG W , LI C , XU Z , et al . Envelope extraction algorithm for amplitude sorting of radar emitter signals [C ] // 2019 IEEE 2nd International Conference on Computer and Communication Engineering Technology (CCET) . Piscataway : IEEE , 2019 : 223 - 228 .

CHEN T , GUO X Q , LI J X . Radar signal sorting method with mimetic image mapping based on antenna scan pattern via SOLOv2 network [J ] . Remote Sensing , 2024 , 16 ( 24 ): 4639 .

WANG C , WANG Y , LI X Q , et al . A deinterleaving method for mechanical-scanning radar signals based on deep learning [C ] // 2022 7th International Conference on Intelligent Computing and Signal Processing . Piscataway : IEEE , 2022 : 138 - 143 .

BARSHAN B , ERAVCI B . Automatic radar antenna scan type recognition in electronic warfare [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2012 , 48 ( 4 ): 2908 - 2931 .

GUO Q , TENG L , WU X L , et al . Deinterleaving of radar pulse based on implicit feature [J ] . Journal of Systems Engineering and Electronics , 2023 , 34 ( 6 ): 1537 - 1549 .

ZHANG Y J , HUO W B , ZHANG C , et al . Radar operation mode recognition via multifeature residual-and-shrinkage ConvNet [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2023 , 16 : 6073 - 6084 .

ESTER M , KRIEGEL H P , SANDER J , et al . A density-based algorithm for discovering clusters in large spatial databases with noise [C ] // Proceedings of the Second International Conference on Knowledge Discovery and Data Mining . New York : ACM , 1996 : 226 - 231 .

GALDINO S , DIAS J . Interval-valued data ward’s hierarchical agglomerative clustering method: Comparison of three representative merge points [C ] // 2021 International Conference on Engineering and Emerging Technologies . Piscataway : IEEE , 2022 : 1 - 6 .

GUO Q , TENG L , QI L G , et al . A novel radar signals sorting method-based trajectory features [J ] . IEEE Access , 2019 , 7 : 171235 - 171245 .

ZHENG H P , XIE K , ZHU Y S , et al . An reconstruction bidirectional recurrent neural network-based deinterleaving method for known radar signals in open-set scenarios [J ] . IET Radar , Sonar & Navigation, 2024 , 18 ( 6 ): 965 - 981 .

FUYUE L I U , JINXIN L I . End-to-end radar signal sorting based on deep segmentation [J ] . Systems Engineering and Electronics , 2023 , 45 ( 5 ): 1351 - 1358 .

ARTHUR D , VASSILVITSKII S . How slow is the k-means method? [C ] // Proceedings of the 22th Annual Aymposium on Computational Geometry . Sedona, Arizona : ACM , 2006 : 144 - 153 .

COMANICIU D , MEER P . Mean shift: A robust approach toward feature space analysis [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2002 , 24 ( 5 ): 603 - 619 .

FREY B J , DUECK D . Clustering by passing messages between data points [J ] . Science , 2007 , 315 ( 5814 ): 972 - 976 .

DEMPSTER A P , LAIRD N M , RUBIN D B . Maximum likelihood from incomplete data via the EM algorithm [J ] . Journal of the Royal Statistical Society. Series B (Methodological) , 1977 , 39 ( 1 ): 1 - 38 .

SHI J B , MALIK J . Normalized cuts and image segmentation [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2000 , 22 ( 8 ): 888 - 905 .

CAMPELLO R J G B , MOULAVI D , SANDER J . Density-based clustering based on hierarchical density estimates [C ] // Advances in Knowledge Discovery and Data Mining . Berlin : Springer , 2013 : 160 - 172 .

KARYPIS G , KUMAR V , STEINBACH M . A comparison of document clustering techniques [C ] // TextMining Workshop at KDD2000 . New York : ACM , 2000 : 428 - 439 .

ZHAO Y , KARYPIS G . Evaluation of hierarchical clustering algorithms for document datasets [C ] // Proceedings of the 11th International Conference on Information and Knowledge Management . New York : ACM , 2002 : 515 - 524 .

FOWLKES E B , MALLOWS C L . A method for comparing two hierarchical clusterings [J ] . Journal of the American Statistical Association , 1983 , 78 ( 383 ): 553 - 569 .

HUBERT L , ARABIE P . Comparing partitions [J ] . Journal of Classification , 1985 , 2 ( 1 ): 193 - 218 .

左峰 , 曹兰英 , 杨健 . 一种基于关联脉冲对的动态直方图分选算法 [J ] . 现代防御技术 , 2023 , 51 ( 1 ): 59 - 66 .

ZUO F , CAO L Y , YANG J . A dynamic histogram deinterleaving algorithm based on correlated pulse pairs [J ] . Modern Defence Technology , 2023 , 51 ( 1 ): 59 - 66 . (in Chinese)

GENÇOL K , KARA A , AT N . Improvements on deinterleaving of radar pulses in dynamically varying signal environments [J ] . Digital Signal Processing , 2017 , 69 : 86 - 93 .

KOCAMıŞ M B , ORDUYıLMAZ A , TAŞCıOĞLU S . Object detection based deinterleaving of radar signals using deep learning for cognitive EW [J ] . Signal, Image and Video Processing , 2024 , 18 ( 11 ): 7789 - 7800 .

：作者简介：

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Continuous Relation Extraction Technique Based on Dynamic Relational Prototypes

Pseudo-label Denoising and SAM Optimization for Large-scale Unsupervised Semantic Segmentation

Heuristic Clustering Algorithm Based on Sub-Game Perfect Equilibrium

Density Peaks Clustering Algorithm Based on Geodesic Distance and Cosine Mutual Reverse Nearest Neighbors for Manifold Datasets

Related Author

LIN Jin-jian

ZHONG Jiang

DAI Qi-zhu

LI Xue

YANG Wei-jing

XU Rui

GU Hao-wen

CHEN Tao

Related Institution

School of Computer Science, Chongqing University

Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University

School of Electrical Engineering and Computer Science, The University of Queensland

School of Computer Science and Engineering, Nanjing University of Science and Technology

School of Computer Science and Engineering， University of Electronics Science and Technology of China

⁰