Comb Jamming Mitigation in Frequency Hopping Spread Spectrum Communications Via Aid Block Sparse Bayesian Learning

Comb jamming is an effective jamming mode for spectrum spread frequency hopping (FHSS) communication. In order to ensure the effectiveness of FHSS communication under strong interference conditions, it is necessary to suppress the comb jamming effectively. The existing FHSS communication comb jamming suppression method has the disadvantage that its application is limited by the high sampling rate. The compressive sensing (CS) is applied to the suppression of comb jamming in FHSS communication in this study. According to the different sparse characteristics of FHSS signal and comb jamming in frequency domain, a block sparse Bayesian learning (BSBL) based FHSS communication comb interference suppression model is constructed. To further improve the performance of the BSBL algorithm used in this model, a BSBL based algorithm, aid BSBL (ABSBL), is proposed exploiting the nature of comb jamming in the frequency domain, where the intra-correlation matrix is modeled as unit matrix. A comb jamming suppression algorithm for FHSS communications is designed based on ABSBL. The efficiency of comb jamming mitigation is improved while keeping the performance of comb jamming mitigation using the proposed algorithm. Besides, the performance of the algorithm does not rely on the block sparse structure information of the comb jamming. The simulation results show the performance of the proposed algorithm could suppress the comb jamming in FHSS communications effectively, and achieves better performance compared to other conventional algorithms.