基于时频方差聚类的FH信号参数盲估计

doi:10.3969/j.issn.1001-506X.2020.08.03

Abstract

Abstract:

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%.

Key words: parameter estimation, genetic algorithm, time-frequency variance, low signal-to-noise ratio(SNR), fixed-frequency interference

CLC Number:

TN911

Shengkui ZHANG, Zhicheng YAO, Min HE, Zhiliang FAN, Jian YANG. FH signal parameter blind estimation based on time-frequency variance clustering[J]. Systems Engineering and Electronics, 2020, 42(8): 1662-1667.

Figures/Tables 11

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

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