基于PDW多特征融合的辐射源信号分选方法

doi:10.12305/j.issn.1001-506X.2024.01.09

系统工程与电子技术 ›› 2023, Vol. 46 ›› Issue (1): 80-87.doi: 10.12305/j.issn.1001-506X.2024.01.09

• 传感器与信号处理 • 上一篇

基于PDW多特征融合的辐射源信号分选方法

罗佳奕¹, 李煊鹏¹^,*, 李江浩², 薛启凡¹, 杨凤¹, 张为公¹

1. 东南大学仪器科学与工程学院, 江苏南京 211189
2. 中国航天科工集团8511研究所, 江苏南京 211100

收稿日期:2021-12-13 出版日期:2023-12-28 发布日期:2024-01-11
通讯作者: 李煊鹏
作者简介:罗佳奕(1997—), 男, 硕士研究生, 主要研究方向为辐射源信号分选和个体识别
李煊鹏(1985—), 男, 副教授, 博士, 主要研究方向为因果推理、机器学习
李江浩(1997—), 男, 硕士研究生, 主要研究方向为电子侦察、无源定位
薛启凡(1995—), 男, 博士研究生, 主要研究方向为智能感知、辐射源信号处理
杨凤(1997—), 女, 博士研究生, 主要研究方向为模式识别、表征学习
张为公(1959—), 男, 教授, 博士, 主要研究方向为机电一体化、系统建模
基金资助:
国家自然科学基金(61906038);中央高校基本科研业务费专项资金(2242021R41184);东南大学“至善青年学者”支持计划

Radiation signal sorting method based on PDW multi-feature fusion

Jiayi LUO¹, Xuanpeng LI¹^,*, Jianghao LI², Qifan XUE¹, Feng YANG¹, Weigong ZHANG¹

1. School of Instrument Science and Engineering, Southeast University, Nanjing 211189, China
2. No.8511 Research Institute, China Aerospace Science and Industry Corporation, Nanjing 211100, China

Received:2021-12-13 Online:2023-12-28 Published:2024-01-11
Contact: Xuanpeng LI

摘要/Abstract

摘要：

针对现有辐射源信号分选技术混叠信号处理困难、分选精度不高的问题, 提出了一种针对脉冲信号描述字的多特征融合分选方法, 采用分治思想, 具备两级分选架构。第一步通过构建时空密度聚类模型, 分离出在时频域上混叠的辐射源信号; 第二步通过多参数交并比方法, 进一步提升算法精度。与传统方法相比, 所提方法能够有效分选出时频域混叠脉冲序列, 受辐射源脉冲重复间隔变化造成的影响较小, 相比传统方法具有更高的准确性。

关键词: 辐射源信号分选, 时空密度聚类, 交并比, 多特征融合, 脉冲描述字

Abstract:

In view of the difficulty to deal with aliasing signals and low accuracy of sorting in the field of radiation signals sorting, this paper proposes a multi-feature fusion method, focusing on pulse description word (PDW), which is a two-step process architecture based on divide-conquer method. In the first step, the problem of radial source signal aliasing in the time-frequency domain is solved by the proposed spatial-time density clustering model. The second step is to further improve the accuracy of sorting through the intersection over union method. Compared with the traditional methods, this method can effectively separate the pulse signal of the mixed time-frequency domain with higher sorting accuracy. It is robust against the variation of pulse repetition interval (PRI) of radiation source.

Key words: radiation signal sorting, spatial-temporal density clustering, intersection over union (IoU), multi-feature fusion, pulse description word (PDW)

中图分类号:

TN911

罗佳奕, 李煊鹏, 李江浩, 薛启凡, 杨凤, 张为公. 基于PDW多特征融合的辐射源信号分选方法[J]. 系统工程与电子技术, 2023, 46(1): 80-87.

Jiayi LUO, Xuanpeng LI, Jianghao LI, Qifan XUE, Feng YANG, Weigong ZHANG. Radiation signal sorting method based on PDW multi-feature fusion[J]. Systems Engineering and Electronics, 2023, 46(1): 80-87.

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参考文献 34

1	ZHAO S Q , WANG W H , ZENG D G , et al. A novel aggregated multipath extreme gradient boosting approach for radar emitter classification[J]. IEEE Trans.on Industrial Electronics, 2021, 69 (1): 703- 712.
2	王俊岭, 黄琰璟. 基于序列关联的参差信号分选算法[J]. 电子与信息学报, 2021, 43 (4): 1145- 1153.
	WANG J L , HUANG D J . Stagger pulse repetition interval pulse train deinterleaving algorithm based on sequence association[J]. Journal of Electronics & Information Technology, 2021, 43 (4): 1145- 1153.
3	LIU Y C , ZHANG Q Y . Improved method for deinterleaving radar signals and estimating PRI values[J]. IET Radar, Sonar & Navigation, 2018, 12 (5): 506- 514.
4	CHENG W H , ZHANG Q Y , DONG J M , et al. An enhanced algorithm for deinterleaving mixed radar signals[J]. IEEE Trans.on Aerospace and Electronic Systems, 2021, 57 (6): 3927- 3940. doi: 10.1109/TAES.2021.3087832
5	陈涛, 王天航, 郭立民. 基于PRI变换的雷达脉冲序列搜索方法[J]. 系统工程与电子技术, 2017, 39 (6): 1261- 1267.
	CHEN T , WANG T H , GUO L M . Sequence searching methods of radar signal pulses based on PRI transform algorithm[J]. Systems Engineering and Electronics, 2017, 39 (6): 1261- 1267.
6	乔宏乐, 王超, 王鹏. 基于PRI变换法的脉冲信号分选算法[J]. 火控雷达技术, 2012, 41 (2): 34- 38. doi: 10.3969/j.issn.1008-8652.2012.02.009
	QIAO H L , WANG C , WANG P . Pulse signals de-interleaving algorithm based on PRI transform[J]. Fire Control Radar Technology, 2012, 41 (2): 34- 38. doi: 10.3969/j.issn.1008-8652.2012.02.009
7	张怡霄, 郭文普, 康凯, 等. 基于数据场联合PRI变换与聚类的雷达信号分选[J]. 系统工程与电子技术, 2019, 41 (7): 1509- 1515.
	ZHANG Y X , GUO W P , KANG K , et al. Radar signal sorting method based on data field combined PRI transform and clustering[J]. Systems Engineering and Electronics, 2019, 41 (7): 1509- 1515.
8	AHMED M G S , TANG B . Sorting radar signal from symmetry clustering perspective[J]. Journal of Systems Engineering and Electronics, 2017, 28 (4): 690- 696. doi: 10.21629/JSEE.2017.04.08
9	ADITYA S , MOLISCH A F , RABEAH N , et al. Localization of multiple targets with identical radar signatures in multipath environments with correlated blocking[J]. IEEE Trans.on Wireless Communications, 2017, 17 (1): 606- 618.
10	DUDCZYK J . A method of feature selection in the aspect of specific identification of radar signals[J]. Bulletin of the Polish Academy of Sciences. Technical Sciences, 2017, 65 (1): 113- 119. doi: 10.1515/bpasts-2017-0014
11	PSORAKIS I , DAMOULAS T , GIROLAMI M A . Multiclass relevance vector machines: sparsity and accuracy[J]. IEEE Trans.on Neural Networks, 2010, 21 (10): 1588- 1598. doi: 10.1109/TNN.2010.2064787
12	LAI C S , JIA Y W , MCCULLOCH M D , et al. Daily clearness index profiles cluster analysis for photovoltaic system[J]. IEEE Trans.on Industrial Informatics, 2017, 13 (5): 2322- 2332. doi: 10.1109/TII.2017.2683519
13	IRANI J , PISE N , PHATAK M . Clustering techniques and the similarity measures used in clustering: a survey[J]. International Journal of Computer Applications, 2016, 134 (7): 9- 14. doi: 10.5120/ijca2016907841
14	LI P . Research on radar signal recognition based on automatic machine learning[J]. Neural Computing and Applications, 2020, 32 (7): 1959- 1969. doi: 10.1007/s00521-019-04494-1
15	CHEN X, LIU D, WANG X, et al. Improved DBSCAN radar signal sorting algorithm based on rough set[C]//Proc. of the 2nd International Conference on Big Data and Informatization Education, 2021: 398-401.
16	REVILLON G , MOHAMMAD-DJAFARI A , ENDERLI C . Radar emitters classification and clustering with a scale mixture of normal distributions[J]. IET Radar, Sonar & Navigation, 2018, 13 (1): 128- 138.
17	DENG Z , HU Y Y , ZHU M , et al. A scalable and fast OPTICS for clustering trajectory big data[J]. Cluster Computing, 2015, 18 (2): 549- 562. doi: 10.1007/s10586-014-0413-9
18	WIJUNIAMURTI S , NUGROHO S , RACHMAWATI R . Agglomerative nesting (AGNES) method and divisive analysis (DIANA) method for hierarchical clustering on some distance measurement concepts[J]. Journal of Statistics and Data Science, 2022, 1 (1): 7- 11. doi: 10.33369/jsds.v1i1.21009
19	XIAO Y H , LIU W J , GAO L P . Radar signal recognition based on transfer learning and feature fusion[J]. Mobile Networks and Applications, 2020, 25 (4): 1563- 1571. doi: 10.1007/s11036-019-01360-1
20	侯文太, 普运伟, 郭媛蒲, 等. 基于高斯平滑与模糊函数等高线的雷达辐射源信号分选[J]. 自动化学报, 2021, 47 (10): 2484- 2493.
	HOU W T , PU Y W , GUO Y P , et al. A sorting method for radar emitter signals based on the Gaussian smoothing and contour lines of ambiguity function[J]. Acta Automatica Sinica, 2021, 47 (10): 2484- 2493.
21	WEI S J , QU Q Z , ZENG X F , et al. Self-attention Bi-LSTM networks for radar signal modulation recognition[J]. IEEE Trans.on Microwave Theory and Techniques, 2021, 69 (11): 5160- 5172. doi: 10.1109/TMTT.2021.3112199
22	PAN M , LIU A L , YU Y Z , et al. Radar HRRP target recognition model based on a stacked CNN-Bi-RNN with attention mechanism[J]. IEEE Trans.on Geoscience and Remote Sensing, 2021, 60, 1- 14.
23	SICA F , GOBBI G , RIZZOLI P , et al. Φ-Net: deep residual learning for InSAR parameters estimation[J]. IEEE Trans.on Geoscience and Remote Sensing, 2020, 59 (5): 3917- 3941.
24	李英达, 肖立志. 一种脉冲重复间隔复杂调制雷达信号分选方法[J]. 电子与信息学报, 2013, 35 (10): 2493- 2497.
	LI Y D , XIAO L Z . A method of signal sorting for radar signal of pulse repetition interval complex modulated[J]. Journal of Electronics & Information Technology, 2013, 35 (10): 2493- 2497.
25	SHEN J B , HAO X P , LIANG Z Y , et al. Real-time superpixel segmentation by DBSCAN clustering algorithm[J]. IEEE Trans.on Image Processing, 2016, 25 (12): 5933- 5942. doi: 10.1109/TIP.2016.2616302
26	FERRARA R , VIRDIS S G P , VENTURA A , et al. An automated approach for wood-leaf separation from terrestrial LIDAR point clouds using the density based clustering algorithm DBSCAN[J]. Agricultural and Forest Meteorology, 2018, 262, 434- 444. doi: 10.1016/j.agrformet.2018.04.008
27	KHAN K, REHMAN S U, AZIZ K, et al. DBSCAN: past, present and future[C]//Proc. of the 5th International Confe-rence on the Applications of Digital Information and Web Technologies, 2014: 232-238.
28	HONG Z H , CHEN Y , MAHMASSANI H S . Recognizing network trip patterns using a spatio-temporal vehicle trajectory clustering algorithm[J]. IEEE Trans.on Intelligent Transportation Systems, 2017, 19 (8): 2548- 2557.
29	闫孟达, 杨任农, 王新, 等. 改进cell密度聚类算法在空战目标分群中的应用[J]. 国防科技大学学报, 2021, 43 (4): 108- 117.
	YAN M D , YANG R N , WANG X , et al. Air combat target grouping based on improved CBSCAN algorithm[J]. Journal of National University of Defense Technology, 2021, 43 (4): 108- 117.
30	REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 658-666.
31	ZENG D G , ZENG X , CHENG H , et al. Automatic modulation classification of radar signals using the Rihaczek distribution and Hough transform[J]. IET Radar, Sonar & Navigation, 2012, 6 (5): 322- 331.
32	CHENG C H , LIN D M , LIOU L L , et al. Electronic warfare receiver with multiple FFT frame sizes[J]. IEEE Trans.on Aerospace and Electronic Systems, 2012, 48 (4): 3318- 3330. doi: 10.1109/TAES.2012.6324709
33	KESHANI S , MASOUMI N . The art of piecewise linear approximation in MMSE estimator for most accurate and fast frequency extraction in DIFM receivers[J]. IEEE Trans.on Instrumentation and Measurement, 2021, 70, 1- 9.
34	YAO Z , CUI X W , LU M Q , et al. Pseudo-correlation-function-based unambiguous tracking technique for sine-BOC signals[J]. IEEE Trans.on Aerospace and Electronic Systems, 2010, 46 (4): 1782- 1796. doi: 10.1109/TAES.2010.5595594

算法	结果
算法	检出率	准确率
DBSCAN	96.23	70.75
OPTICS	98.87	74.24
AGNES	90.58	76.38
本文方法	99.48	81.35

算法	结果
算法	有效数据点占比
DBSCAN	77.26
OPTICS	89.36
AGNES	84.67
本文方法	93.41

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基于PDW多特征融合的辐射源信号分选方法

Radiation signal sorting method based on PDW multi-feature fusion

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