基于信息辅助块稀疏贝叶斯学习的直扩通信窄带干扰抑制

现有基于Nyquist-Shannon采样定理的窄带干扰(Narrowband Interference,NBI)抑制方法存在应用受限于采样率较高的问题。应用压缩感知(Compressive Sensing,CS)理论解决上述问题，利用NBI在频域表现出的块稀疏特性以及直接序列扩频(Direct Sequence Spread Spectrum,DSSS)信号的类噪声特性，提出了基于块稀疏贝叶斯学习(Block Sparse Bayesian Learning,BSBL)框架的DSSS通信NBI抑制模型。实现干扰抑制后，利用传统的CS重构算法实现DSSS信号的压缩域解调。为进一步提高算法性能，将NBI稀疏分块的块内自相关矩阵建模为单位矩阵，提出了信息辅助BSBL(Aid BSBL,ABSBL)算法，设计了基于ABSBL的DSSS通信NBI抑制算法。该算法在保持较好NBI抑制性能的条件下，提高了运算效率并且不依赖NBI的稀疏结构。仿真验证和对比分析结果表明，所提方法能够有效抑制DSSS通信中的NBI，在干扰强度相同的条件下，NBI带宽越小、压缩率越大，算法对NBI的抑制性能越好。

DSSS Communications Narrowband Interference Mitigation via Aid Block Sparse Bayesian Learning

The existing narrowband interference(NBI) mitigation algorithms based on Nyquist-Shannon sampling theory are confined to the high sampling rate.The compressive sensing(CS) theory is applied to the solution of the problem.By using the block sparsity nature of NBI and noise-like nature of direct sequence spread spectrum(DSSS) signal in the frequency domain,a block sparse Bayesian leaning(BSBL) framework based DSSS communication NBI mitigation model is proposed.After the NBI mitigation,the traditional CS algorithm is used to achieve the DSSS signal demodulation in the compressed domain.In order to further improve the performance of the method,a BSBL based algorithm,aid BSBL(ABSBL),is proposed,where the intra-correlation matrix is modeled as unit matrix.An NBI mitigation algorithm for DSSS communications based on ABSBL is designed.The efficiency of NBI mitigation is improved while the performance of NBI mitigation is kept by using the proposed algorithm.Besides,the algorithm does not rely on the prior information of the NBI block sparse structure.Comprehensive analysis and comparison of the algorithm demonstrate that the proposed methods are effective in cancelling the NBI in DSSS communications.When the interference intensity is the same,the narrower the NBI and the greater the compression ratio,the better the NBI mitigation performance.