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基于卡尔曼滤波压缩感知的超宽带信道估计 被引量：1

Ultra Wideband Channel Estimation Based on Kalman Filter Compressed Sensing
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摘要 针对超宽带通信系统采样速率过高的难题,利用超宽带信道冲击响应的稀疏性,提出了一种基于卡尔曼滤波压缩感知的时变信道估计算法.通过将直接序列调制的超宽带发送信号进行下采样,建立压缩感知的数学模型,接收端通过卡尔曼滤波压缩感知的重构算法对信道的冲击响应进行重构.仿真结果表明,对于时变的超宽带信道采用卡尔曼滤波压缩感知算法,不仅可以有效降低采样点数,而且提高了信道估计的准确性. Considering the sparsity of the channel impulse response,a novel time-varying channel estimation approach based on Kalman filter compressed sensing（KF-CS） is proposed to deal with the high sampling problem of ultra wideband（UWB） system.The direct sequence UWB signal is formulated to the mathematical model of compressed sensing after down sampling.The receiver recovers the channel impulse response by Kalman filter compressive sensing algorithm.The simulation results demonstrate that the proposed scheme can reduce the quantity of required sampling points and improve the accuracy of the estimation.
作者 石磊 周正 唐亮
机构地区 北京邮电大学信息与通信工程学院
出处 《北京理工大学学报》 EI CAS CSCD 北大核心 2012年第2期170-173,183,共5页 Transactions of Beijing Institute of Technology
基金 国家重大科技专项资助项目(2009ZX03006009) 韩国知识经济部项目(NIPA-2011-C1090-1111-0007)
关键词 超宽带 信道估计 压缩感知 卡尔曼滤波 ultra wideband channel estimation compressed sensing Kalman filter
分类号 TN911.5 [电子电信—通信与信息系统]
  • 相关文献

参考文献7

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二级参考文献13

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共引文献18

同被引文献16

  • 1邱恺,黄国荣,陈天如,杨亚莉.卡尔曼滤波过程的稳定性研究[J].系统工程与电子技术,2005,27(1):33-35. 被引量：22
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  • 5Eiwen D. Compressive channel estimation-compressed sens- ing methods for estimating doubly selective channels in mul- ticarrier systems [ D ]. [ PhD Thesis ]. University of Vienna,2012.
  • 6Vaswani N, Lu W. Recursive reconstruction of sparse sig- nal sequences [ M ]. Compressed Sensing & Sparse Filte- ring. Springer Berlin Heidelberg, 2014: 357-380.
  • 7Kalouptsidis N, Mileounis G, Babadi B, et al. Adaptive algorithms for sparse system identification [ J ]. Signal Processing, 2011,91(8): 1910-1919.
  • 8Gui G, Mehbodniya A, Adachi F. Sparse LMS/F algorithms with application to adaptive system identification [ J ]. Wireless Communications and Mobile Computing,2015,15 (12) :1649-1658.
  • 9Gui G, Adachi F. Stable adaptive sparse filtering algo- rithms for estimating MIMO channels[J]. IET Communi- cations, 2014, 8(7) :1032-1040.
  • 10Hu D, Wang X, He L. Anew sparse channel estimation and tracking method for time-varying OFDM systems[ J]. IEEE Transactions on Vehicular Technology, 2013, 62 (9) : 4648-4653.

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北京理工大学学报
2012年 第2期

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