›› 2012, Vol. ›› Issue (06): 1587-1591.

• 论文 • 上一篇    下一篇

基于压缩感知的自适应干扰抑制算法

康荣宗, 于宏毅, 田鹏武, 郭虹   

  1. 信息工程大学 信息工程学院, 郑州 450002
  • 收稿日期:2011-09-05 出版日期:2012-11-01
  • 通讯作者: 于宏毅(1963-),男,教授,博士生导师.研究方向:压缩感知,无线自组网,协作通信,网络编码.E-mail:maxyucn@sohu.com E-mail:maxyucn@sohu.com
  • 基金资助:
    "973"国家重点基础研究发展规划项目(613148);国家科技重大专项项目(2008ZX03006).

Adaptive interference mitigation algorithm based on compressed sensing

KANG Rong-zong, YU Hong-yi, TIAN Peng-wu, GUO Hong   

  1. Institute of Information Engineering, Information Engineering University, Zhengzhou 450002, China
  • Received:2011-09-05 Online:2012-11-01

摘要: 针对基于Nyquist采样理论的干扰抑制算法在宽频带信号接收处理中受限于信号带宽和现有器件水平的问题,提出了一种压缩域的自适应干扰抑制算法。首先利用压缩感知技术以远低于Nyquist采样率的速率获得信号的压缩测量值;然后基于最小输出能量准则,并结合空间投影技术,利用投影值完成干扰信号的检测和抑制;进一步推导并给出了算法的闭式解及LMS自适应实现过程。理论分析和仿真结果表明,该算法极大地降低了对A/D器件和后端数字处理器件的要求,且无需预知干扰信号在宽频带中的位置先验信息,并对干扰源的个数无限制,具有较强的实用性。

关键词: 通信技术, 压缩感知, 干扰抑制, 压缩域滤波, 变换域技术

Abstract: In receiving and processing of wide bandwidth signals, the existing interference mitigation algorithms based on Nyquist theory are confined to the signal bandwidth and device level. To overcome this problem, an algorithm of adaptive interference mitigation in compressed domain is proposed. First, the compressed sensing technology is utilized to acquire the compressed measurements of wide bandwidth mixtures at a sampling rate that is far lower than Nyquist sampling rate. Then, based on minimal output energy criteria and space projection technology, the projected values are used to detect and mitigate the interference signals. Furthermore, the closed-form solution and the LMS realization process of this algorithm are deduced. Theoretical analysis and simulation results show that this algorithm reduces the requirement of the A/D and DSP devices; it does not need prior information about the location of the expected signal and interference signals in the wide spectrum bandwidth and has no restriction on the number of the interference signals; so this algorithm has better practicability.

Key words: communication, compressed sensing, interference mitigation, compressive domain filtering, transform domain technique

中图分类号: 

  • TN911.72
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