FH signal parameter blind estimation based on time-frequency variance clustering

被引：0
|
作者：
Zhang S. [1 ]
Yao Z. [1 ]
He M. [2 ]
Fan Z. [1 ]
Yang J. [1 ]
机构：
[1] School of Missile and Engineering, Rocket Force University of Engineering, Xi'an
[2] Beijing Institute of Remote Sensing Equipment, Beijing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 08期
关键词：
Fixed-frequency interference; Genetic algorithm; Low signal-to-noise ratio(SNR); Parameter estimation; Time-frequency variance;
D O I：
10.3969/j.issn.1001-506X.2020.08.03
中图分类号：
学科分类号：
摘要：
In order to solve the blind estimation problem of the frequency hopping (FH) parameters in the complex electromagnetic environment, an algorithm based on time-frequency variance clustering is proposed. Considering the case where both low signal-to-noise ratio (SNR) and fixed frequency interference exist simultaneously, the signal is transformed into the time-frequency domain by short-time Fourier transform (STFT). The time-frequency interval of the signal is extracted by genetic algorithm, and k-means clustering is performed according to the time-frequency variance. Eliminating noise and fixed-frequency interference, and then extracting time-frequency ridge, the Haar wavelet is used to detect its singularities, furthermore, to estimate parameters such as FH period, hopping speed and hopping frequency. The simulation results show that the proposed algorithm can accurately estimate the parameters such as FH period when the SNR is lower than -5 dB. The correct probability of parameter estimation is over 90%. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：1662 / 1667
页数：5
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