J4 ›› 2013, Vol. 40 ›› Issue (6): 1-5+139.doi: 10.3969/j.issn.1001-2400.2013.06.001

• 研究论文 •    下一篇

一种压缩域下的跳频信号盲识别新方法

吴俊;刘乃安;沈常林;张妍飞   

  1. (西安电子科技大学 综合业务网理论及关键技术国家重点实验室,陕西 西安  710071)
  • 收稿日期:2012-08-07 出版日期:2013-12-20 发布日期:2014-01-10
  • 通讯作者: 吴俊
  • 作者简介:吴俊(1987-),男,西安电子科技大学硕士研究生,E-mail: wujun20061008@126.com.
  • 基金资助:

    国家自然科学基金资助项目(61201134)

Novel method for frequency hopping signals blind  identification in the compressed domain

WU Jun;LIU Naian;SHEN Changlin;ZHANG Yanfei   

  1. (State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an  710071, China)
  • Received:2012-08-07 Online:2013-12-20 Published:2014-01-10
  • Contact: WU Jun

摘要:

针对通信对抗中的跳频信号盲识别问题,提出了一种压缩域下的跳频信号盲识别方法.利用该方法直接处理压缩采样值,可以完成跳频信号识别任务.首先深入分析了压缩采样值在接收信号中有/无跳频信号两种不同假设下的数学期望的差异,将跳频信号采样值与其在各个假设下数学期望的偏差作为判决依据,完成识别任务,然后在不重构的前提条件下仅利用低速率压缩测量向量实现跳频频率的估计.仿真实验表明,该方法在信噪比高于-2dB环境下具有良好的识别效果,其频率归一化均方误差可以达到10-4量级,具有较高的频率估计精度.此外,相比于其他识别方法,该方法大大降低了数据量和算法复杂度,显著缩短了识别时间.

关键词: 压缩采样, 跳频, 识别, 频率估计

Abstract:

A novel method to blindly identify frequency hopping signals is presented in the compressed domain. The samples obtained by compressive sampling effectively maintain the structure of and the information on the original signal, so the task of identification of the original signal could be done by directly processing the sampling values. The method is based on the difference in numerical characteristic between sampling values. According to the different characteristics of the expectation of sampling values under different hypotheses, identification is accomplished by using the deviation of the actual sampling values from the expectations under the corresponding hypothesis as the criterion. Without reconstructing the frequency hopping signal itself,hopping frequencies can be estimated through a small number of measurements by the compressive sampling algorithm. Simulation results have proved that the proposed method is adequate to the environments in which the signal-to-noise ratio is higher than -2dB. Meanwhile, compared with other traditional methods, the proposed algorithm greatly reduces the amount of data, the computational complexity, and the identification time.

Key words: compressive sampling, frequency hopping, identification, frequency estimation

中图分类号: 

  • N33