Semi-Blind Separation of Multiple Asynchronous Wideband Frequency Hopping Signals Based on MWC and Spectral Entropy Method
الموضوعات : Majlesi Journal of Telecommunication DevicesMohsen Rezaee 1 , Morteza Babaei 2 , Mohammadreza Motedayen 3
1 - Assistant Professor ICT Research Institute, Tehran, Iran
2 - Assistant Professor Faculty of Electrical Engineering Department, Imam Hossein University, Tehran, Iran
3 - Phd Candidate، Imam Hossein Comprehensive University
الکلمات المفتاحية: Semi-Blind Separation, Modulated Wideband Converter, Time-Frequency Distribution, Spectral Entropy, Wideband Frequency-Hopping Signal.,
ملخص المقالة :
Wideband Frequency Hopping Spread Spectrum (FHSS) communications are widely used in both military and commercial applications. In military applications, it is very important to investigate these communications, especially when frequency hopping signals are received simultaneously by a single antenna. This paper investigates the problem of estimating interfering wideband asynchronous frequency hopping (FH) signals parameters with the same hop rate, and using narrow-band receivers. Due to minimal knowledge about the transmitted signals, the problem is analyzed in semi-blind mode. For this purpose, time-frequency (TF) processing has been applied to the modulated wideband converter (MWC) output. The proposed method consists of two stages; In the first stage, frequency-hopping signals with different amplitudes are received by a single antenna. By passing through baseband receivers, the TF distribution of the converter's output signal is obtained. In the next stage, by computing instantaneous spectral entropy (SE), and finding the local maxima in the spectrum, the hop time of each signal is obtained. We use MWC for sub-Nyquist sampling and simultaneous extraction of time and frequency information from signals while eliminating irrelevant signals. The results obtained from estimating hop time parameters demonstrate improved performance compared to other traditional source separation methods such as sparse linear regression (SLR). Based on evaluation metrics such as root mean squared error (RMSE), in lower signal-to-noise ratio (SNR) values, the amount of error has been substantially reduced.
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