基于块稀疏贝叶斯学习的跳频通信梳状干扰抑制

doi:10.3969/j.issn.1000-1093.2018.09.025

兵工学报 ›› 2018, Vol. 39 ›› Issue (9): 1864-1872.doi: 10.3969/j.issn.1000-1093.2018.09.025

• 研究简报 • 上一篇

基于块稀疏贝叶斯学习的跳频通信梳状干扰抑制

张永顺¹，朱卫纲²，孟祥航³，贾鑫²，曾创展¹，王满喜⁴

(1.航天工程大学研究生院, 北京 101416; 2.航天工程大学电子与光学工程系, 北京 101416;3.航天工程大学科研学术处, 北京 101416; 4.电子信息系统复杂电磁环境效应国家重点实验室, 河南洛阳 471003)

收稿日期:2018-02-02 修回日期:2018-02-02 上线日期:2018-10-25
作者简介:张永顺（1991—），男，博士研究生。E-mail：zysguida@163.com
基金资助:
中央军事委员会科学技术委员会国防科技创新特区项目（17-H863-01-ZT-003-207-XX）

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

ZHANG Yong-shun¹， ZHU Wei-gang²， MENG Xiang-hang³， JIA Xin²， ZENG Chuang-zhan¹， WANG Man-xi⁴

(1.Graduate School, Space Engineering University, Beijing 101416, China;2.Department of Electronic and Optical Engineering, Space Engineering University, Beijing 101416, China;3.Office of Scientific and Academic Research, Space Engineering University, Beijing 101416, China;4.State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, Henan, China)

Received:2018-02-02 Revised:2018-02-02 Online:2018-10-25

摘要/Abstract

摘要： 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。

关键词: 跳频通信, 梳状干扰抑制, 压缩感知, 块稀疏, 块稀疏贝叶斯学习-期望最大化算法

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

中图分类号:

TN975

张永顺，朱卫纲，孟祥航，贾鑫，曾创展，王满喜. 基于块稀疏贝叶斯学习的跳频通信梳状干扰抑制[J]. 兵工学报, 2018, 39(9): 1864-1872.

ZHANG Yong-shun， ZHU Wei-gang， MENG Xiang-hang， JIA Xin， ZENG Chuang-zhan， WANG Man-xi. Comb Jamming Mitigation in Frequency-hopping Spread Spectrum Communications via Block Sparse Bayesian Learning[J]. Acta Armamentarii, 2018, 39(9): 1864-1872.

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基于块稀疏贝叶斯学习的跳频通信梳状干扰抑制

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

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