非高斯码元检测的马尔可夫链蒙特卡洛算法
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摘要
针对实际超低频接收机不仅受非高斯噪声的影响,还受接收机内部和外部环境中高斯噪声影响的问题,对噪声采用非高斯分布和高斯分布的混合模型建模,根据混合模型的性质,设计了一种利用马尔可夫链蒙特卡洛方法的超低频信号码元盲检测算法。盲检测算法在贝叶斯层次模型下,采用Gibbs抽样和M-H抽样更新参数,同步估计信道衰落系数和噪声模型参数,并实现对信号码元的检测。算法迭代效率快、精度高。通过与最优检测算法性能比较,盲检测算法性能优异,对超低频信号接收具有重要的现实意义。
Considering that the receiver was not only affected by the non-Gaussian noise but also affected by its internal and external environment of Gaussian noise,a mixed model composed by non-Gaussian distribution plus Gaussian distribution was proposed. A blind detection algorithm based on Markov Chain Monte Carlo method was designed according to the properties of the mixed model. The blind detection algorithm could estimate the channel fading coefficient,parameters of noise model and could detect signal element. Detect signals based on Bayesian hierarchical model was using Gibbs sample and M- H sample for parameter updating. The algorithm has a high iterative efficiency and precision.Results show that the proposed blind detection algorithm performs as well as the optimal detection algorithm and has important realistic significance in super low-freguency signal reception.
Considering that the receiver was not only affected by the non-Gaussian noise but also affected by its internal and external environment of Gaussian noise,a mixed model composed by non-Gaussian distribution plus Gaussian distribution was proposed. A blind detection algorithm based on Markov Chain Monte Carlo method was designed according to the properties of the mixed model. The blind detection algorithm could estimate the channel fading coefficient,parameters of noise model and could detect signal element. Detect signals based on Bayesian hierarchical model was using Gibbs sample and M- H sample for parameter updating. The algorithm has a high iterative efficiency and precision.Results show that the proposed blind detection algorithm performs as well as the optimal detection algorithm and has important realistic significance in super low-freguency signal reception.
引文
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[2]Buzzi S,Conte E,Lops M.Optimum detection over rayleighfading,dispersive channels,with non-Gaussian noise[J].IEEE Transactions on Communications,1997,45(9):1061-1069.
[3]Buzzi S,Conte E,Lops M.Signal detection over rayleighfading channels with non-Gaussian noise[J].IEE Proceedings Communications,1997,144(6):381-386.
[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.YING Wenwei,JIANG Yuzhong,LIU Yueliang.Parameters estimation for mixture model of atmospheric noise through MCMC method[J].Systems Engineering Electronics,2012,34(6):1241-1245.(in Chinese)
[5]Ying W W,Jiang Y Z,Liu Y L,et al.A novel adaptive receiver for flat fading channels with impulsive noise[C]//Proceedings of IEEE 12th International Conference on Computer Information Technology,2012:631-634.
[6]Ollila E,Tyler D E,Koivunen V,et al.Compound-Gaussian clutter modeling with an inverse gaussian texture distribution[J].IEEE Signal Processing Letters,2012,19(12):876-879.
[7]Greco M,Gini F,Diani M.Robust CFAR detection of random signals in compound-Gaussian clutter plus thermal noise[J].IEE Proceedings Radar,Sonar Navigation,2001,148(4):227-232.
[8]Wang J,Dogandzic A,Nehorai A.Maximum likelihood estimation of compound-Gaussian clutter 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,2013,35(2):376-380.ZHAO Yinan,LI Fengcong,YIN Bin.Adaptive polarimetric detection of targets in heavy-tailed compound-Gaussian clutter[J].Journal of Electronics&Information Technology,2013,35(2):376-380.(in Chinese)
[10]Balleri A,Nehorai A,Wang J.Maximum likelihood estimation for compound-Gaussian clutter with inverse gamma texture[J].IEEE Transactions on Aerospace Electronic Systems,2007,43(2):775-779.
[11]Geng P,Huang Z T,Wang F H,et al.Single channel blind signal separation with Bayesian-MCMC[C]//Proceedings of International Conference on Wireless Communications&Signal Processing,IEEE,2009:1-4.
[12]Lee A,Yau C,Giles M B,et al.On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods[J]Journal of Computational Graphical Statistics,2010,19(4):769-789.