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基于形态学滤波和时频谱图对消的多跳频信号参数估计
2021年电子技术应用第12期
刘佳敏1,赵知劲1,2,叶学义1,王李军2
1.杭州电子科技大学 通信工程学院,浙江 杭州310018; 2.中国电子科技集团第36研究所 通信系统信息控制技术国家级重点实验室,浙江 嘉兴314001
摘要:针对复杂电磁环境下多跳频信号的参数估计问题,提出一种基于多尺度形态学滤波和时频谱图对消的信号参数盲估计算法。首先根据跳频信号、干扰和噪声的时频特征差异性,采用多尺度形态学滤波消除噪声、突发和扫频信号,并利用谱图对消法剔除定频信号;然后通过八连通域标记获取跳频信号的位置信息,利用改进的K-means聚类算法实现异速跳频信号的分离;最后由各类簇参数估计多跳频信号的周期、跳变时刻和跳频频率。仿真结果表明,与利用形态学滤波并提取时频脊线的方法相比,该算法在低信噪比下具有更高的估计精度,且在定频、跳频信号发生频率碰撞时,仍能准确估计跳频参数。
中图分类号:TN914.41
文献标识码:A
DOI:10.16157/j.issn.0258-7998.211459
中文引用格式:刘佳敏,赵知劲,叶学义,等. 基于形态学滤波和时频谱图对消的多跳频信号参数估计[J].电子技术应用,2021,47(12):83-88.
英文引用格式:Liu Jiamin,Zhao Zhijin,Ye Xueyi,et al. Morphological filtering and time-spectrogram cancellation based parameters estimation algorithm of multi FH signals[J]. Application of Electronic Technique,2021,47(12):83-88.
Morphological filtering and time-spectrogram cancellation based parameters estimation algorithm of multi FH signals
Liu Jiamin1,Zhao Zhijin1,2,Ye Xueyi1,Wang Lijun2
1.School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China; 2.State Key Lab of Information Control Technology in Communication System, The 36th Research Institute of China Electronics Technology Group Corporation,Jiaxing 314001,China
Abstract:Aiming at estimating the parameters of multi-frequency hopping signals in complex electromagnetic environment, a blind estimation algorithm of signals parameters based on multi-scale morphological filtering and time-spectrogram cancellation is proposed. Firstly, considering the characteristic differences of frequency hopping signals, interference signals and noise, multi-scale morphological filtering is used to eliminate the noise, frequency sweep signals and burst signals, the time-spectrogram cancellation is used to remove the fixed-frequency signals. Then the position information of the frequency hopping signals is obtained through the eight-connected domain mark, and then the all-speed frequency hopping signals are separated by the improved K-means clustering algorithm. Finally, the period, hopping time and frequency of multiple frequency hopping signals are estimated according to the parameters of each class cluster. The simulation results show that compared with the estimation algorithm that uses morphological filtering and extracts the time-frequency ridge, the proposed algorithm has higher estimation accuracy under low signal-to-noise ratio, and even though the frequency collision between frequency hopping signals and the fixed frequency signals occurs, the frequency hopping parameters can still be estimated accurately.
Key words :frequency hopping signal;parameter estimation;morphological filtering;time-spectrogram cancellation;clustering

0 引言

跳频信号因其具有较强的抗干扰、抗截获和抗衰落等能力,被广泛应用于军事通信[1]。跳频信号参数估计是通信侦察的主要任务之一,随着电磁环境的日益复杂,参数精确估计变得愈发困难。当前,跳频信号参数估计算法可分为非时频分析法和时频分析法两大类,由于大多数非时频分析法需要已知一些特定条件,且只能估计出跳频信号的部分参数,因此简单直观且无需先验信息的时频分析法更适用于跳频信号的参数盲估计。文献[2]通过两次短时傅里叶变换(Short-Time Fourier Transform,STFT)估计信号跳变时刻并利用多重信号分类算法进行频率估计,具有较高估计精度,但无法兼顾时间和频率分辨率。文献[3]将STFT和平滑伪魏格纳-威尔分布(Smoothed Pseudo Wigner-Ville Distribution,SPWVD)相结合,在提高时频分辨率的同时抑制了交叉项干扰,但只适用于同步网台的跳频信号参数估计,且部分参数估计精度受信号能量分布影响严重。文献[4]~[8]通过提取跳频信号时频脊线进行参数估计,文献[8]在文献[7]的基础上利用迭代法去噪和基于驻留时长的K-means聚类进行参数估计,提升了算法抗噪性能,但在定频信号与跳频信号发生频率碰撞时算法失效。为了提升参数估计性能,文献[9]~[12]在不同时频变换的基础上引入图像处理技术,采用形态学滤波对时频图像进行处理后,提取时频脊线完成跳频信号的参数估计,相比于直接提取时频脊线的参数估计法,这些方法具有更高的精确度,但都仅针对单个跳频信号的参数估计。文献[2]~[11]仅考虑了存在高斯白噪声或定频信号的简单电磁环境,而文献[12]在较强干扰背景下算法性能发生恶化甚至失效。




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作者信息:

刘佳敏1,赵知劲1,2,叶学义1,王李军2

(1.杭州电子科技大学 通信工程学院,浙江 杭州310018;

2.中国电子科技集团第36研究所 通信系统信息控制技术国家级重点实验室,浙江 嘉兴314001)




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