基于非重构压缩采样的动态稀疏信号快速检测技术研究

J4 ›› 2012, Vol. 34 ›› Issue (4): 123-127.

基于非重构压缩采样的动态稀疏信号快速检测技术研究

朱勇刚，李永贵，郭明

（总参第63研究所,江苏南京 210007)

收稿日期:2011-11-05 修回日期:2012-02-10 出版日期:2012-04-26 发布日期:2012-04-25
基金资助:
通信信息控制和安全技术重点实验室基金资助项目（9140C13030111DZ4603)；国家重大专项资助项目(2010ZX03006-002-01)

Fast Detection of the Dynamic Sparse Signals Based on Compressive Sampling Without Signal Recovery

ZHU Yonggang，LI Yonggui，GUO Ming

(The 63rd Research Institute of PLA General Staff Headquarters，Nanjing 210007,China)

Received:2011-11-05 Revised:2012-02-10 Online:2012-04-26 Published:2012-04-25

摘要/Abstract

摘要：

战场电磁环境的快速、准确感知对于提高战术无线通信系统的时变电磁环境适应能力和抗干扰能力具有重要意义。本文分析了跳频通信信号、扫频干扰信号等战场常见的多种通信信号与干扰信号的动态稀疏特性，构建了动态稀疏信号检测的统一框架。在此基础上，提出了基于非重构压缩采样的动态稀疏信号快速检测技术的基本思路，并分析了该方法的检测性能界。分析结果表明：该方法不仅能够充分利用动态稀疏信号的稀疏特性，大大降低采样速率和后续分析与处理中的数据量，而且避免了复杂的信号重构，能够有效降低动态稀疏信号检测的处理时延，提高了战场电磁环境感知的实时性。

关键词: 动态稀疏信号；检测；压缩采样；非重构

Abstract:

Fast spectrum sensing is the cornerstone of tactical wireless communication systems to adapt to the changing electromagnetic environments and improves its antijamming ability. The dynamic sparse characteristics of many communication signals and jamming signals, such as the frequencyhopping signal and chirp jamming signal, are analyzed, and the uniform framework of the detection of dynamic sparse signals is established. Based on that, a novel algorithm for fast detection of dynamic sparse signals based on compressive sampling without signal recovery is improved, and its detection performance is analyzed. There are two virtues of the new algorithm. Firstly, the sparse feature is employed to diminish the sampling speed and to reduce the data amount for the following detection, analysis, and identification. Secondly, and the construction of the original signal is avoided, the computer complexity of which is very high.

Key words: dynamic sparse signal;detection;compressive sampling;nonconstruction

朱勇刚，李永贵，郭明. 基于非重构压缩采样的动态稀疏信号快速检测技术研究[J]. J4, 2012, 34(4): 123-127.

ZHU Yonggang，LI Yonggui，GUO Ming. Fast Detection of the Dynamic Sparse Signals Based on Compressive Sampling Without Signal Recovery[J]. J4, 2012, 34(4): 123-127.