Semi-Blind Separation of Multiple Synchronous Wideband Frequency Hopping Signals Using a Band-Limited Receiver and Space-Time- Frequency Distributions

Babaei, Morteza; Motedayen, Mohammadreza; Rezaee, Mohsen

Semi-Blind Separation of Multiple Synchronous Wideband Frequency Hopping Signals Using a Band-Limited Receiver and Space-Time- Frequency Distributions

Document Type : Original Article

Authors

¹ Electronic Warfare Department, Faculty of Electrical and Electronics Engineering, Imam Hussein University, Tehran, IRAN

² Satellite Communications Group, Faculty of Communication Technology, ICT Research Institute, Tehran, IRAN

Abstract

Frequency hopping spread spectrum (FHSS) communications are used widely in military and commercial communications. Therefore, estimating the frequency hopping parameters of signals is of great importance. This article examines the problem of separating and estimating the parameters of multiple synchronous and time-interfering frequency hopping wideband signals using receivers with low bandwidth and low sampling rate. Due to minimal knowledge about the transmitted signals, the problem is analyzed in semi-blind mode. For this purpose, a hybrid method based on array processing is developed based on the modulated broadband converter (MWC) and processing in the time-frequency domain by considering the spatial information of the signal. The above method consists of two steps: the first step is receiving frequency hopping signals by uniform linear arrays (ULA) and passing through the MWC with minimal components. the next step, the joint diagonal (JD) method is applied to the space-time-frequency distributions (STFDs) of the MWC output data. This leads to the estimation of the separator matrix and finally to the separation of the signals and the extraction of the frequency hopping pattern. The combined MWC-STF-JD method is the idea used in solving the problem, where MWC features and processing in the STF domain are used simultaneously. The results of the estimation of the desired parameters, compared to other traditional methods of source separation such as Compressed Sensing (CS) analysis based on evaluation indices such as root mean square error (RMSE), have led to improved performance in lower signal-to-noise ratio (SNR). So that the amount of error in the range of SNR=4 dB reaches the minimum error faster by 6 dB. Among the other features of this method, it is possible to reduce the complexity of STFDs processing calculations because of the low sampling rate MWC and the possibility of implementing the design with minimal components.

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