基于改进DBSCAN算法的跳频网台分选方法

doi:10.12305/j.issn.1001-506X.2026.07.32

摘要/Abstract

摘要：

针对复杂电磁环境下跳频网台分选面临的强噪声和干扰抑制难题，提出了一种基于联合特征的改进基于密度的带噪声应用空间聚类(density-based spatial clustering of applications with noise，DBSCAN）算法。借鉴点云建模的思想，以参数估计得到的各跳段跳频周期、跳变时刻与平均能量为特征，构建联合特征向量，将跳频信号的特征抽象为结构化的点云分布，在此基础上，计算联合特征的距离矩阵以确定参数k，并结合直方图密度划分生成对应的邻域半径和邻域内最小点数，遍历每组序列进行迭代聚类，实现对多跳频信号簇结构的自动提取与融合，解决了传统DBSCAN方法对密度分布敏感的问题。仿真实验表明，所提方法在信噪比为?2 dB条件下分选准确率达98.5%，显著优于K-Means、K均值层次方法及传统DBSCAN算法的准确率。该方法无需依赖跳频参数先验信息，具备良好的抗干扰能力与泛化性能，为复杂电磁环境下的跳频信号分选提供了思路。

关键词: 跳频通信, 基于密度的带噪声应用空间聚类, 时频分析, 参数估计, 跳频网台分选

Abstract:

Addressing the challenge of strong noise and interference suppression in frequency-hopping network station sorting under complex electromagnetic environments, an improved density-based spatial clustering of applications with noise (DBSCAN) algorithm based on joint features is proposed. Inspired by point cloud modeling, a joint feature vector is constructed using the estimated hop segment parameters—namely, hop period, hopping instant, and average energy—abstracting the frequency hopping signal features into a structured point cloud distribution. On this basis, a joint feature distance matrix is computed to determine the parameter k, while a histogram-based density partitioning method is employed to generate the corresponding neighborhood radius and minimum number of points within the neighborhood. An iterative clustering process is then performed by traversing each sequence, enabling automatic extraction and fusion of multiple frequency hopping signal clusters. This approach effectively overcomes the sensitivity of traditional DBSCAN to density distribution. Simulation results demonstrate that the proposed method achieves a sorting accuracy of 98.5% at a signal to noise ratio of ?2 dB, significantly outperforming K-Means, K-means hierarchical method, and conventional DBSCAN algorithms. The method does not require prior knowledge of frequency hopping parameters and exhibits strong anti-interference capability and generalization performance, offering a solution for frequency hopping signal sorting in complex electromagnetic environments.

Key words: frequency-hopping communication, density-based spatial clustering of applications with noise clustering （DBSCAN）, time-frequency analysis, parameter estimation, frequency-hopping network station selection

中图分类号:

TN92

周伟, 郭浩南, 侯长波, 孟昊, 万凯. 基于改进DBSCAN算法的跳频网台分选方法[J]. 系统工程与电子技术, 2026, 48(7): 2492-2500.

Wei ZHOU, Haonan GUO, Changbo HOU, Hao MENG, Kai WAN. Method for sorting frequency-hopping network stations based on the improved DBSCAN algorithm[J]. Systems Engineering and Electronics, 2026, 48(7): 2492-2500.

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参考文献 32

1	YANG Y C, SUN X L, ZHONG Z G, et al. Frequency-hopping network station sorting based on improved independent component analysis[C]//Proc. of the 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, 2018: 2397−2402.
2	FU W H, HU Z, LI D. A sorting algorithm for multiple frequency-hopping signals in complex electromagnetic environments[J]. Circuits, Systems, and Signal Processing, 2020, 39 (1): 245- 267. doi: 10.1007/s00034-019-01160-8
3	张盛魁, 姚志成, 何岷, 等. 改进时频脊线的跳频参数盲估计算法[J]. 系统工程与电子技术, 2019, 41 (12): 2885- 2890.
	ZHANG S K, YAO Z C, HE M, et al. Blind estimation algorithm for frequency hopping parameters of improved time-frequency ridge[J]. Systems Engineering and Electronics, 2019, 41 (12): 2885- 2890.
4	FU W H, LI X H, LIU N A, et al. Parameter blind estimation of frequency-hopping signal based on time–frequency diagram modification[J]. Wireless Personal Communications, 2017, 97 (3): 3979- 3992. doi: 10.1007/s11277-017-4710-5
5	GUIDO R C. Practical and useful tips on discrete wavelet transforms [sp tips & tricks][J]. IEEE Signal Processing Magazine, 2015, 32 (3): 162- 166. doi: 10.1109/MSP.2014.2368586
6	YANG W Y, LI M, WANG L B, et al. Parameter estimation of frequency hopping signals based on time frequency analysis[C]//Proc. of the 26th Conference of Spacecraft TT&C Technology in China: Shared and Flexible TT&C (Tracking, Telemetry and Command) Systems, 2012: 131−140.
7	ZHANG H X, CHEN C F, WANG H Q. A parameter estimation method for FH signal based on SPWVD[J]. The Journal of China Universities of Posts and Telecommunications, 2011, 18, 133- 136. doi: 10.1016/S1005-8885(10)60136-7
8	YANG Y C, SUN X L, ZHONG Z G. A parameter estimation algorithm for frequency-hopping signals with a stable noise[C]//Proc. of the IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, 2018: 1898−1904.
9	卢云生, 董英英. 基于STFT的跳频信号参数估计[J]. 舰船电子工程, 2011, 31 (10): 3.
	LU Y S, DONG Y Y. Parameter estimation of frequency-hopping signals based on STFT[J]. Ship Electronic Engineering, 2011, 31 (10): 3.
10	陈利虎, 张尔扬, 沈荣骏. 跳频信号的时频分析[J]. 宇航学报, 2009 (2): 740- 747.
	CHEN L H, ZHANG E Y, SHEN R J. Time-frequency analysis of frequency-hopping signals[J]. Journal of Astronautics, 2009 (2): 740- 747.
11	ZHANG X Y, FAN Z Z, SUN F H, et al. A fractional domain modulation method based on frequency hopping mechanism[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025, 42 (3): 11- 15.
12	李琰, 王露, 李天昀, 等. 短波复杂信道下跳频信号的参数估计[J]. 信息工程大学学报, 2011, 12 (2): 168- 173.
	LI Y, WANG L, LI T Y, et al. Parameter estimation of frequency-hopping signals in shortwave complex channels[J]. Journal of Information Engineering University, 2011, 12 (2): 168- 173.
13	冯涛, 袁超伟. 基于时频脊线的跳频参数盲估计[J]. 电子学报, 2011, 39 (12): 2921- 2925.
	FENG T, YUAN C W. Blind estimation of frequency-hopping parameters based on time-frequency ridge[J]. Acta Electronica Sinica, 2011, 39 (12): 2921- 2925.
14	SARKAR T K, PEREIRA O. Using the matrix pencil method to estimate the parameters of a sum of complex exponentials[J]. IEEE Antennas and Propagation Magazine, 1995, 37 (1): 48- 55. doi: 10.1109/74.370583
15	ROY R, PAULRAJ A, KAILATH T. Comparative performance of ESPRIT and MUSIC for direction-of-arrival estimation[C]//Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1987.
16	GHOSH A, DONG A, HAIMOVICH A, et al. Blind source separation of intermittent frequency hopping sources over LOS and NLOS channels[J]. Entropy, 2023, 25 (9): 1292. doi: 10.3390/e25091292
17	YANG Y X, XU C, ZHAO X Y, et al. Joint estimation method for space-time frequency parameters of frequency-hopping network station in the case of low-quality data[J]. Computer Communications, 2025: 108234.
18	WANG Z Y, ZHANG B B, ZHU Z Y, et al. Signal sorting algorithm of hybrid frequency hopping network station based on neural network[J]. IEEE Access, 2021, 9, 35924- 35931. doi: 10.1109/ACCESS.2021.3062361
19	ZENG Z Z, JIANG C S, ZHOU Y M, et al. A time–frequency domain analysis method for variable frequency hopping signal[J]. Sensors （Basel, Switzerland）, 2024, 24 (19): 6449. doi: 10.3390/s24196449
20	WANG Y, LIAO H H, YUAN S, et al. A learning-based signal parameter extraction approach for multi-source frequency-hopping signal sorting[J]. IEEE Signal Processing Letters, 2023, 30, 1162- 1166. doi: 10.1109/LSP.2023.3309161
21	岳峰巍. 跳频通信网台分选方法的研究与仿真实现[D]. 成都: 电子科技大学, 2011.
	YUE F W. Research and simulation implementation of frequency-hopping network station sorting method [D]. Chengdu: University of Electronic Science and Technology of China, 2011.
22	YE J Q, ZOU J, GAO J, et al. A new frequency hopping signal detection of civil UAV based on improved K-means clustering algorithm[J]. IEEE Access, 2021, 9, 53190- 53204. doi: 10.1109/ACCESS.2021.3070491
23	HE D J, WU X R, ZHENG X, et al. Frequency hopping signal sorting based on spectrum monitoring data by adaptive dbscan[C]//Proc. of the 7th International Conference on Signal and Image Processing, 2022: 16−22.
24	SARBU A, ȘORECĂU M, ȘORECĂU E, et al. Frequency hopping signals tracking and sorting algorithm for military radio networks[C]//Proc. of the International Symposium on Electromagnetic Compatibility–EMC Europe, 2023.
25	AHMAD A A, AJI M M, KOLAWOLE S F, et al. Frequency estimations of normal and frequency hopping radar signals using Wigner-Ville distributions[C]//Proc. of the IEEE 5th International Conference on Electro-Computing Technologies for Humanity, 2024.
26	SU Z J, WANG R, ZHAO Z Z. Estimation of hopping rate of frequency hopping signal with low SNR based on wavelet transform[C]//Proc. of the 2nd International Conference on Artificial Intelligence and Information Systems, 2021.
27	SERRA J, VINCENT L. An overview of morphological filtering[J]. Circuits, Systems and Signal Processing, 1992, 11 (1): 47- 108.
28	BAI J S, YAO J J, YANG Z L, et al. Transient electromagnetic weak signal extraction method based on multi-scale combined difference product morphological filtering[J]. IEEE Access, 2022, 10, 22140- 22149. doi: 10.1109/ACCESS.2022.3145974
29	WANG Y Y, HE S R, WANG C R, et al. Detection and parameter estimation of frequency hopping signal based on the deep neural network[J]. International Journal of Electronics, 2022, 109 (3): 520- 536. doi: 10.1080/00207217.2021.1914190
30	LIU C K, WU X R, ZHU L, et al. The communication relationship discovery based on the spectrum monitoring data by improved DBSCAN[J]. IEEE Access, 2019, 7, 121793- 121804. doi: 10.1109/ACCESS.2019.2938296
31	SCITOVSKI R, SABO K. DBSCAN-like clustering method for various data densities[J]. Pattern Analysis and Applications, 2020, 23 (2): 541- 554. doi: 10.1007/s10044-019-00809-z
32	ABDULHAFEDH A. Incorporating k-means, hierarchical clustering and PCA in customer segmentation[J]. Journal of City and Development, 2021, 3 (1): 12- 30.

算法类型	CA/%	NMI	耗时/s
K-Means	65.3	0.713	0.132
KHM	82.1	0.847	0.342
DBSCAN	85.4	0.879	0.401
本文	94.1	0.953	0.884

信号类型	参数设置
FH信号1	FH周期：4 ms
FH信号2	FH周期：6 ms
定频信号	数量1~3个
扫频信号	数量1个，扫描周期8 ms，带宽50 kHz
猝发信号	数量1~5个

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