基于块稀疏贝叶斯学习的直扩通信窄带干扰检测与参数估计
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摘要
现有基于Nyquist采样定理的直扩(direct sequence spread spectrum,DSSS)通信窄带干扰(narrowband interference,NBI)检测和参数估计方法存在应用受限于采样率较高的问题。针对这一问题,将压缩感知(compressive sensing,CS)应用于DSSS通信NBI的检测和参数估计,根据DSSS信号与NBI的不同压缩域特性以及NBI在频域表现出的分块稀疏特性,利用块稀疏贝叶斯学习(block sparse Bayesian leaning,BSBL)框架获取干扰检测和参数估计的特征量,通过对特征量的检测和参数估计实现对NBI的检测和参数估计。理论分析和仿真结果表明:所提方法能够在压缩采样条件下实现对DSSS通信中NBI的有效检测和参数估计,与传统方法相比具有显著优势,干扰检测和参数估计性能受干扰强度、干扰带宽以及压缩率变化的影响,干扰强度越强、干扰带宽越小、压缩率越大,干扰检测和参数估计效果越好。
The existing narrowband interference(NBI)detection and parameter estimation algorithms for direct sequence spread spectrum(DSSS)communications based on the Nyquist sampling theorem are confined to the high sampling rate.In order to solve this problem,the compressive sensing is used to the NBI detection and parameter estimation in DSSS communications,a newly emerged sparse approximation technique,block sparse Bayesian learning,is utilized to get the NBI feature vector from the compressed signal using the different features of DSSS signals and NBI in the compressed domain and the block sparsity feature of NBI in the frequency domain.The NBI detection and parameter estimation are realized by detecting and estimating parameters of the feature vector.Reported simulation results demonstrate that the proposed method is effective in the NBI detection and parameter estimation in DSSS communications,and significantly outperforms other conventional methods.The performance is mainly affected by the variety of interference intensity,interference bandwidth and compression ratio.The larger the interference intensity is,the smaller the interference bandwidth is and the greater the compression ratio is,the better the interference detection and parameter estimation performance are.
The existing narrowband interference(NBI)detection and parameter estimation algorithms for direct sequence spread spectrum(DSSS)communications based on the Nyquist sampling theorem are confined to the high sampling rate.In order to solve this problem,the compressive sensing is used to the NBI detection and parameter estimation in DSSS communications,a newly emerged sparse approximation technique,block sparse Bayesian learning,is utilized to get the NBI feature vector from the compressed signal using the different features of DSSS signals and NBI in the compressed domain and the block sparsity feature of NBI in the frequency domain.The NBI detection and parameter estimation are realized by detecting and estimating parameters of the feature vector.Reported simulation results demonstrate that the proposed method is effective in the NBI detection and parameter estimation in DSSS communications,and significantly outperforms other conventional methods.The performance is mainly affected by the variety of interference intensity,interference bandwidth and compression ratio.The larger the interference intensity is,the smaller the interference bandwidth is and the greater the compression ratio is,the better the interference detection and parameter estimation performance are.
引文
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[2]CANDES E J,ROMBERG J,TAO T.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J].IEEE Trans.on Information Theory,2006,52(2):489-509.
[3]CANDES E J,TAO T.Near-optimal signal recovery from random projections:universal encoding strategies[J].IEEE Trans.on Information Theory,2006,52(12):5406-5425.
[4]CHATZIANTONIOU E,ALLEN B,VELISAVLJEVIC V,et al.Energy detection based spectrum sensing over two-wave and diffuse power fading channels[J].IEEE Trans.on Vehicular Technology,2017,66(1):868-874.
[5]WEI L,TIRKKONEN O,LIANG Y C.Multi-source signal detection with arbitrary noise covariance[J].IEEE Trans.on Signal Processing,2014,62(22):5907-5918.
[6]NADLER B,PENNA F,GARELLO R.Performance of eigenvaluebased signal detectors with known and unknown noise level[C]∥Proc.of the IEEE International Conference on Communications,2011:1-5.
[7]DUARTE M F,DAVENPORT M A,WAKIN M B,et al.Sparse signal detection from Incoherent projections[C]∥Proc.of the IEEE International Conference on Acoustics,Speech and Signal Processing,2006:1-2.
[8]刘冰,付平,孟升卫.基于正交匹配追踪的压缩感知信号检测算法[J].仪器仪表学报,2010,31(9):1959-1964.LIU B,FU P,MENG S.Compressive sensing signal detection algorithm based on orthogonal matching pursuit[J].Chinese Journal of Scientific Instrument,2010,31(9):1959-1964.
[9]王莹桂.压缩采样信号检测及多任务重构算法研究[D].长沙:国防科技大学,2014.WANG Y G.Research on compressive signal detection and multitast recovery algorithms[D].Changsha:National University of Defense Technology,2014.
[10]DAVENPORT M A,WAKIN M B,BARANIUK R G.Detection and estimation with compressive measurements[R].Houston:Rice University,2007.
[11]付云红,张云飞,韦娟,等.基于压缩感知的跳频信号参数盲估计算法[J].通信学报,2017,38(12):48-56.FU Y H,ZHANG Y F,WEI J,et al.Blind parameter estimation algorithm for frequency-hopping signals based on compressed sensing[J].Journal on Communications,2017,38(12):48-56.
[12]PANAHI A,VIBERG M.Performance analysis of sparsitybased parameter estimation[J].IEEE Trans.on Signal Processing,2017,65(24):6478-6488.
[13]TIAN Z,GIANNAKIS G B.A Wavelet approach to wideband spectrum sensing for cognitive radios[C]∥Proc.of the International Conference on Cognitive Radio Oriented Wireless Networks&Communications,2006:1-5.
[14]马宁,王建新,董宁斐.基于正交匹配追踪的欠采样LFM信号参数估计[J].电子与信息学报,2013,35(8):1888-1893.MA N,WANG J,DONG N.Parameter estimation of subsampling LFM signal based on orthogonal matching pursuit[J].Journal of Electronics&Information Technology,2013,35(8):1888-1893.
[15]ZHANG Z,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.
[16]LI G,GU Y.Restricted isometry property of Gaussian random projection for finite set of subspaces[J].IEEE Trans.on Signal Processing,2017,66(7):1705-1720.
[17]SHAMSI M,REZAII T Y,TINATI M A,et al.Block sparse signal recovery in compressed sensing:optimum active block selection and within-block sparsity order estimation[J].Circuits Systems&Signal Processing,2017,37(4):1-20.
[18]WANG X,LI G,LIU Y,et al.Two-level block matching pursuit for polarimetric through-wall radar imaging[J].IEEE Trans.on Geoscience&Remote Sensing,2018,56(3):1533-1545.
[19]MA X,YANG F,LIU S,et al.Sparse channel estimation for MIMO-OFDM systems in high-mobility situations[J].IEEETrans.on Vehicular Technology,2018,67(7):6113-6124.
[20]TIPPING M E.Sparse Bayesian learning and relevance vector machine[J].Journal of Machine Learning Research,2001,1:211-244.
[21]MAHROUS H,WARD R.Block sparse compressed sensing of electroencephalogram(EEG)signals by exploiting linear and non-linear dependencies[J].Sensors,2016,16(2):1-16.
[22]SHI H,ZHANG H,LI G,et al.Stable embedding of grassmann manifold via Gaussian random matrices[J].IEEE Trans.on Information Theory,2015,61(5):2924-2941.