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基于自适应形态学的跳频信号参数联合盲估计 被引量:7

Joint blind parameter estimation of frequency hopping signal based on adaptive morphology
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摘要 跳频通信的应用大大提高了军事装备的抗干扰和抗截获能力,使得跳频对抗技术面临严峻的挑战。为解决传统形态学跳频信号参数估计方法中结构元素选择困难问题并提高估计精度,提出了一种基于自适应形态学的跳频信号参数联合盲估计方法。首先,对跳频信号进行短时傅里叶变换获取谱图。然后,从其时间轴投影中获取结构元素尺寸的知识,设计自适应形态学滤波器抑制谱图噪声,提取跳频图案初步估计跳频参数。最后,引入最小二乘估计方法,对跳频周期和跳变时刻进行精估计。仿真结果表明,此方法能够同时估计出跳频频率、跳频周期和跳变时刻,不需要其中某一种参数作为先验条件,在复杂的通信环境也能够保持良好的估计性能。 The application of frequency hopping communication improves the anti-jamming and anti-interception ability of military equipment greatly,making the frequency hopping countermeasure technology to face severe challenges.In order to find a way out of the difficulty in selecting structural elements in traditional parameter estimation method of frequency hopping signal based on morphological and to improve the estimation accuracy,a joint blind parameter estimation method of frequency hopping(FH)signal based on adaptive morphology is presented.Firstly,the short time Fourier transform(STFT)is used to obtain the spectrogram.Then,the knowledge of structural element size is acquired from time axis projection of the spectrogram,an adaptive morphological filter is designed to suppress the noise.And the FH pattern is extracted,from which the FH parameters are preliminarily estimated.Finally,the least square estimation(LSE)is introduced for more accurate estimation of hopping period and hopping time.Simulation results show that hopping frequency,hopping period and hopping time can be estimated at the same time,and no prior knowledge is needed.The method maintains good estimation performance even in complex communication environment.
作者 王曼颖 龚晓峰 雒瑞森 边彤 王之远 WANG Manying;GONG Xiaofeng;LUO Ruisen;BIAN Tong;WANG Zhiyuan(College of Electrical Engineering, Sichuan University, Chengdu 610065, China)
出处 《系统工程与电子技术》 EI CSCD 北大核心 2021年第5期1398-1405,共8页 Systems Engineering and Electronics
基金 四川省重点研发计划项目(20ZDYF3113) 校企合作项目(17H1199,19H0355)资助课题。
关键词 跳频信号 参数估计 短时傅里叶变换 形态学 最小二乘估计 frequency hopping(FH)signal parameter estimation short time Fourier transform(STFT) morphology least square estimation(LSE)
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