Abstract: Comb jamming is a common interference pattern in frequency-hopping spread spectrum (FHSS) communications. Comb jamming mitigation is a very important issue to ensure the effectiveness of FHSS communications. The existing comb jamming mitigation algorithms for FHSS communications are confined to the high sampling rate. In order to solve the problem above, the compressive sensing (CS) is applied to the comb jamming mitigation in FHSS communications. A comb jamming mitigation model based on block sparse Bayesian learning (BSBL) is established using the different features of FHSS signal and comb jamming in compressed domain and the block sparsity feature of comb jamming in frequency domain. A FHSS communications comb jamming mitigation algorithm based on BSBL_EM is designed using the expectation maximization (EM) algorithm. The algorithm uses the BSBL_EM to reconstruct the comb jamming from the compressed data, and then cancel the interference in time domain. Simulated results demonstrate that the proposed methods can effectively suppress the comb jamming in FHSS communications, and significantly outperform other conventional methods. The jamming mitigation performance is mainly affected by the variety of interference intensity, comb jamming bandwidth and compression rate. Under the condition of same interference intensity, the narrower the comb jamming bandwidth is and the greater the compression rate is, the better the jamming mitigation performance is. Key

Key words: frequency-hoppingspread-spectrumcommunication, combjammingmitigation, compressivesensing, blocksparsity, BSBL_EMalgorithm