基于块稀疏贝叶斯学习的跳频通信梳状干扰抑制
|
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。
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 designedusing 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.
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 designedusing 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.
引文
[1]Zander J,Malmgren G.Adaptive frequency hopping in HF communications[J].IEE Proceedings-Communications,1995,142(2):99-105.
[2]Herrick D L,Lee P K.CHESS a new reliable high speed HF radio[C]∥Proceedings of IEEE Military Communications Conference.Mc Lean,VA,US:IEEE,1996:684-690.
[3]Candès E J,Romberg J,Tao T.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory,2006,52(2):489-509.
[4]Candes E J,Tao T.Near-optimal signal recovery from random projections:universal encoding strategies?[J].IEEE Transactions on Information Theory,2006,52(12):5406-5425.
[5]Liu S,Yang F,Zhang C,et al.Narrowband interference mitigation based on compressive sensing for OFDM systems[J].IEICE Transactions on Fundamentals of Electronics Communications&Computer Sciences,2015,E98-A(3):870-873.
[6]Liu S,Yang F,Song J,et al.Block sparse bayesian learning based NB-Io T interference elimination in LTE-advanced systems[J].IEEE Transactions on Communications,2017,65(10):4559-4571.
[7]康宗荣,田鹏武,于弘毅.一种基于选择性测量的自适应压缩感知方法[J].物理学报,2014,63(20):139-146.KANG Zong-rong,TIAN Peng-wu,YU Hong-yi.An adaptive compressed sensing method based on selective measure[J].Acta Physica Sinica,2014,63(20):139-146.(in Chinese)
[8]文方青,张弓,陶宇,等.面向低信噪比的自适应压缩感知方法[J].物理学报,2015,64(8):084301-1-084301-8.WEN Fang-qing,ZHANG Gong,TAO Yu,et al.Adaptive compressive sensing toward low signal-to-noise ratio scene[J].Acta Physica Sinica,2015,64(8):084301-1-084301-8.(in Chinese)
[9]Zhang Y S,Jia X.Adaptive interference suppression for DSSS communications based on compressive sensing[J].International Journal of Communication Systems,2018,31(11):e3699.
[10]Eldar Y C,Bolcskei H.Block-sparsity:coherence and efficient recovery[C]∥Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processing.Taipei:IEEE,2009:2885-2888.
[11]Zhang Z L,Rao B D.Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J].IEEE Journal of Selected Topics in Signal Processing,2011,5(5):912-926.
[12]Candès E J.The restricted isometry property and its implications for compressed sensing[J].Comptes Rendus Mathematique,2008,346(9):589-592.
[13]Baraniuk R G.A lecture on compressive sensing[J].IEEE Signal Processing Magazine,2007,24(4):118-121.
[14]Tipping M E.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2011,1(3):211-244.
[15]Kushary D.The EM algorithm and extensions[J].Technometrics,2008,40(3):260.
[16]Zhang Z L,Rao B D.Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation[J].IEEE Transactions on Signal Processing,2012,61(8):2009-2015.
[17]Arias-Castro E,Eldar Y C.Noise folding in compressed sensing[J].IEEE Signal Processing Letters,2011,18(8):478-481.
[18]王伟,唐伟民,王犇,等.基于贝叶斯压缩感知的复数稀疏信号恢复方法[J].电子与信息学报,2016,38(6):1419-1423.WANG Wei,TANG Wei-min,WANG Ben,et al.A parse signal recovery based on complex Bayesian compressive sensing[J].Journal of Electronics&Information Technology,2016,38(6):1419-1423.(in Chinese)
[2]Herrick D L,Lee P K.CHESS a new reliable high speed HF radio[C]∥Proceedings of IEEE Military Communications Conference.Mc Lean,VA,US:IEEE,1996:684-690.
[3]Candès E J,Romberg J,Tao T.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory,2006,52(2):489-509.
[4]Candes E J,Tao T.Near-optimal signal recovery from random projections:universal encoding strategies?[J].IEEE Transactions on Information Theory,2006,52(12):5406-5425.
[5]Liu S,Yang F,Zhang C,et al.Narrowband interference mitigation based on compressive sensing for OFDM systems[J].IEICE Transactions on Fundamentals of Electronics Communications&Computer Sciences,2015,E98-A(3):870-873.
[6]Liu S,Yang F,Song J,et al.Block sparse bayesian learning based NB-Io T interference elimination in LTE-advanced systems[J].IEEE Transactions on Communications,2017,65(10):4559-4571.
[7]康宗荣,田鹏武,于弘毅.一种基于选择性测量的自适应压缩感知方法[J].物理学报,2014,63(20):139-146.KANG Zong-rong,TIAN Peng-wu,YU Hong-yi.An adaptive compressed sensing method based on selective measure[J].Acta Physica Sinica,2014,63(20):139-146.(in Chinese)
[8]文方青,张弓,陶宇,等.面向低信噪比的自适应压缩感知方法[J].物理学报,2015,64(8):084301-1-084301-8.WEN Fang-qing,ZHANG Gong,TAO Yu,et al.Adaptive compressive sensing toward low signal-to-noise ratio scene[J].Acta Physica Sinica,2015,64(8):084301-1-084301-8.(in Chinese)
[9]Zhang Y S,Jia X.Adaptive interference suppression for DSSS communications based on compressive sensing[J].International Journal of Communication Systems,2018,31(11):e3699.
[10]Eldar Y C,Bolcskei H.Block-sparsity:coherence and efficient recovery[C]∥Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processing.Taipei:IEEE,2009:2885-2888.
[11]Zhang Z L,Rao B D.Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J].IEEE Journal of Selected Topics in Signal Processing,2011,5(5):912-926.
[12]Candès E J.The restricted isometry property and its implications for compressed sensing[J].Comptes Rendus Mathematique,2008,346(9):589-592.
[13]Baraniuk R G.A lecture on compressive sensing[J].IEEE Signal Processing Magazine,2007,24(4):118-121.
[14]Tipping M E.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2011,1(3):211-244.
[15]Kushary D.The EM algorithm and extensions[J].Technometrics,2008,40(3):260.
[16]Zhang Z L,Rao B D.Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation[J].IEEE Transactions on Signal Processing,2012,61(8):2009-2015.
[17]Arias-Castro E,Eldar Y C.Noise folding in compressed sensing[J].IEEE Signal Processing Letters,2011,18(8):478-481.
[18]王伟,唐伟民,王犇,等.基于贝叶斯压缩感知的复数稀疏信号恢复方法[J].电子与信息学报,2016,38(6):1419-1423.WANG Wei,TANG Wei-min,WANG Ben,et al.A parse signal recovery based on complex Bayesian compressive sensing[J].Journal of Electronics&Information Technology,2016,38(6):1419-1423.(in Chinese)