非高斯噪声下信号盲检测算法
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摘要
针对实际甚低频和超低频接收机受非高斯噪声影响的问题,采用有限混合高斯噪声模型,建立了信号检测模型,并设计了一种基于马尔可夫链蒙特卡罗方法的信号盲检测算法。盲检测算法在贝叶斯层次模型下,采用Gibbs抽样更新参数,同步检测信道衰落系数、噪声模型参数和信号,该算法迭代速度快、精度高。通过与理论误码率性能比较,盲检测算法性能优异,对甚低频和超低频信号接收具有重要的现实意义。
Considering receiver in very low frequency( VLF) and super low frequency( SLF) communication system is affected by the non- Gaussian noise,a signal detection model with the finite mixture Gaussian noise model was proposed. A blind detection algorithm based on Markov Chain Monte Carlo( MCMC) algorithm was designed. The blind detection algorithm can detect the channel fading coefficient,parameters of noise model,and signals at the same time through Gibbs sampler,which was based on Bayesian hierarchical model. The algorithm has a high iterative efficiency and precision. The results suggest that the error rate of the proposed blind detection algorithm is as well as the theoretical error rate and can be excellently applied in practice.
Considering receiver in very low frequency( VLF) and super low frequency( SLF) communication system is affected by the non- Gaussian noise,a signal detection model with the finite mixture Gaussian noise model was proposed. A blind detection algorithm based on Markov Chain Monte Carlo( MCMC) algorithm was designed. The blind detection algorithm can detect the channel fading coefficient,parameters of noise model,and signals at the same time through Gibbs sampler,which was based on Bayesian hierarchical model. The algorithm has a high iterative efficiency and precision. The results suggest that the error rate of the proposed blind detection algorithm is as well as the theoretical error rate and can be excellently applied in practice.
引文
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[10]BALLERI A,NEHORAI A,WANG J.Maximum likelihood estimation for compound-Gaussian clutter with inverse gamma texture[J].IEEE Transactions on Aerospace and Electronic Systems,2007,43(2):775-780.
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[2]ESA O,DAVID E T,VISA K,et al.Compound-Gaussian clutter modeling with an inverse Gaussian texture distribution[J].IEEE Signal Processing Letters,2012,19(12):876-879.
[3]GRECO M,GINI F,DIANI M.Robust CFAR detection of random signals in compound-Gaussian clutter plus thermal noise[J].Radar Sonar and Navigation IEE Proceedings,2001,148(4):227-232.
[4]应文威,蒋宇中,刘月亮.大气低频噪声混合模型的MCMC参数估计[J].系统工程与电子技术,2012,34(6):1241-1245.
[5]CONTE E,BISCEGLIE M D,LOPS M.Optimum detection of fading signals in impulsive noise[J].IEEE Transactions on Communications,1995,43(2):869-876.
[6]BUZZI S,CONTE E,LOPS M.Optimum detection over Rayleigh-fading dispersive channels with nonGaussian noise[J].IEEE Transactions on Communications,1997,45(9):1061-1069.
[7]BUZZI S,CONTE E,LOPS M.Signal detection over Rayleigh-fading channels with non-Gaussian noise[J].IEEProc-Commun,1997,144(6):381-386.
[8]WANG J,ALEKSANDAR D,ARYE N.Maximum likelihood estimation of compound-Gaussian clutter and target parameters[J].IEEE Transactions on Signal Processing,2006,54(10):3884-3898.
[9]赵宜楠,李风从,尹彬.严重拖尾复合高斯杂波中目标的自适应极化检测[J].电子与信息学报,2013,5(2):376-380.
[10]BALLERI A,NEHORAI A,WANG J.Maximum likelihood estimation for compound-Gaussian clutter with inverse gamma texture[J].IEEE Transactions on Aerospace and Electronic Systems,2007,43(2):775-780.
[11]PENG G,HUANG Z T,WANG F H,et al.Single channel blind signal separation with Bayesian-MCMC[C]//Wireless Communications&Signal Processing.China:IEEE,2009:1-4.
[12]ANTHONY L,CHRISTOPHER Y,MICHAEL B G,et al.On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods[J].Journal of Computational and Graphical Statistics,2010,19(4):769-789.