期刊文献+

应用基扩展模型的混合信号单通道盲分离算法 被引量:4

A Single Channel Blind Separation Algorithm for Mixed Signals Applying the Base Expansion Model
下载PDF
导出
摘要 针对传统最小均方误差逐幸存路径处理(LMS-PSP)单通道盲分离算法在时变信道下性能差的问题,提出一种基于基扩展模型逐幸存路径处理(BEM-PSP)的单通道盲分离算法。首先对接收到的部分混合信号进行LMS-PSP单通道盲分离,得到部分准确的信道冲激响应(CIR);然后结合时变信道下基于基扩展模型进行信道估计的思想,完成整个时间周期CIR的估计;最后采用Viterbi算法对混合信号进行序列估计,从而实现时变信道下混合信号的单通道盲分离。仿真结果表明,对于2路混合QPSK信号,在相同仿真条件下,BEM-PSP算法较LMS-PSP算法能降低50%的复杂度且能获得更好的性能,在20dB处的误码率可达4×10-2,而LMS-PSP单通道盲分离算法的误码率只能达到1×10-1,并且在同等过采样倍数下,该算法能获得更高的性能提升。 A new algorithm based on basis expansion model per-survivor processing(BEM-PSP)is proposed to overcome the poor performance of the traditional least mean square per-survivor processing(LMS-PSP)algorithm for single channel blind separation in time-varying channels.First,aportion of accurate channel impulse response(CIR)is obtained through processing a portion of received mixed signals.Then,estimations of the CIR during the whole time period is accomplished by combining the channel estimation using the basis expansion model.Finally,the Viterbi algorithm is applied to estimate sequences to the mixed signals,and the single channel blind separation of the mixed signals is accomplished.Simulation results and a comparison with the LMS-PSP single channel blind separation algorithm in same simulation conditions show that the complexity of the proposed algorithm reduces by 50% with better performance in processing mixed QPSK signals,and that the proposed algorithm achieves a bit error rate of 4×10-2 while the LMS-PSP algorithm only achieves a bit error rate of 1×10-1 when the signal to noise ratio is20 dB.It is also observed that the proposed algorithm obtains a higher performance improvement with the same oversampling ratio.
出处 《西安交通大学学报》 EI CAS CSCD 北大核心 2015年第6期60-66,共7页 Journal of Xi'an Jiaotong University
基金 国家自然科学基金资助项目(61271104)
关键词 单通道 盲分离 逐幸存路径处理 基扩展模型 Viterbi序列估计 single channel blind separation per-survivor processing basis expansion model Viterbi sequence estimation
  • 相关文献

参考文献13

二级参考文献96

  • 1蔡权伟,魏平,肖先赐.基于模型拟合的重叠信号盲分离方法[J].电子学报,2005,33(10):1794-1798. 被引量:6
  • 2肖文书,张兴敢,都思丹.雷达信号的盲分离[J].南京大学学报(自然科学版),2006,42(1):38-43. 被引量:25
  • 3Forney G D. Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference[J]. IEEE Trans. on Information Theory, 1972, IT-18(3): 363-378.
  • 4Li Yuanqing, Amari S, Cichocki A, Daniel W C H. and Xie Shengli. Undetermined blind source separation based on Sparse Representation[J]. IEEE Trans. on Signal Processing, 2006, 54(2): 423-437.
  • 5Theis F J, Lang E W, and Puntonet C G. A geometric algorithm for overcomplete linear ICA[J]. Neurocomputing, 2004, 56: 381-398.
  • 6Heidari S and Nikias C L. Co-channel interference mitigation in the time-scale domain: the CIMTS algorithm[J]. IEEE Trans. on Signal Processing, 1996, 44(9): 2151-2162.
  • 7Warner E S and Proudler I K. Single-channel blind signal separation of filtered MPSK signals[J]. IEEE Proceedings, Radar, Sonar and Navigation, 2003, 150(6): 396-402.
  • 8Liu Kai, Li Hui, Dal Xuchu, and Xu Xiaodong. Single channel blind signal separation of cofrequency MPSK signals[C]. Proceedings of International Conference on Communication, Internet and Information Technology (CIIT 2006), St. Thomas, USVI, USA, Nov. 2006: 42-46.
  • 9Arulampalam M S, Maskell S, Gordon N, and Clapp T. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking[J]. IEEE Trans. on Signal Processing, 2002 50(2): 174-188.
  • 10Liu J S and Chen R. Sequential monte carlo methods for dynamic systems[J]. Journal of American Statistical Association, 1998, 93(443): 1032-1043.

共引文献109

同被引文献30

引证文献4

二级引证文献20

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部