基于块稀疏贝叶斯学习的跳频通信梳状干扰抑制

梳状干扰是对跳频(FHSS)通信的一种有效干扰样式，抑制梳状干扰对于确保FHSS通信的有效性至关重要。现有基于奈奎斯特采样定理的梳状干扰抑制方法存在应用中受限于采样率较高的问题。将压缩感知(CS)应用于FHSS通信梳状干扰的抑制，利用FHSS信号与梳状干扰的不同压缩域特性以及梳状干扰在频域表现出的块稀疏特性，建立了基于块稀疏贝叶斯学习(BSBL）框架的FHSS通信梳状干扰抑制模型。利用期望最大化(EM)算法，设计了基于BSBL_EM的FHSS通信梳状干扰抑制算法。该算法利用BSBL_EM算法从压缩采样数据中重构出梳状干扰，进而在时域对消干扰。仿真结果表明：所提方法能够有效地抑制FHSS通信中的梳状干扰，与传统方法相比具有显著优势，干扰抑制效果受干扰强度、干扰梳齿带宽和压缩率变化的影响；相同干扰强度条件下，梳齿带宽越窄，压缩率越大，干扰抑制效果越好。

Comb Jamming Mitigation in Frequency-hopping Spread Spectrum Communications via Block Sparse Bayesian Learning

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.