电子学报 ›› 2014, Vol. 42 ›› Issue (10): 1932-1937.DOI: 10.3969/j.issn.0372-2112.2014.10.010

• 学术论文 • 上一篇    下一篇

基于时频稀疏性的跳频信号背景噪声估计算法

辛吉荣1,2, 陆路希2, 包昕2, 程建2   

  1. 1. 国防科学技术大学电子科学与工程学院, 湖南长沙 410073;
    2. 盲信号处理重点实验室, 四川成都 610041
  • 收稿日期:2013-06-04 修回日期:2013-11-12 出版日期:2014-10-25 发布日期:2014-10-25
  • 作者简介:辛吉荣 男,生于1985年10月,陕西宝鸡人.2012年考入国防科学技术大学电子科学与工程学院,现为在读博士生.主要研究方向:通信信号处理、阵列信号处理.;陆路希 男,生于1982年9月,上海人.2011年6月获北京大学博士学位,现任盲信号处理实验室工程师,主要研究方向:跳扩频信号处理、稀疏信号处理.;包 昕 男,生于1986年,成都人,盲信号处理实验室博士研究生,研究方向:信道编码识别、稀疏信号处理.;程 建 男,生于1964年,重庆人,盲信号处理实验室高级工程师,研究方向:卫星通信.

Noise Energy Estimator Based on Sparseness of Time-Frequency Domain for Broadband Frequency-Hopping Signal

XIN Ji-rong1,2, LU Lu-xi2, BAO Xin2, CHENG Jian2   

  1. 1. College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China;
    2. National Key Laboratory of Blind Signals Processing, Chengdu, Sichuan 610041, China
  • Received:2013-06-04 Revised:2013-11-12 Online:2014-10-25 Published:2014-10-25

摘要:

背景噪声估计是自适应盲信号检测的依据.针对传统背景噪声估计复杂度高不适用于宽带卫星跳频信号盲处理系统的现实问题,提出了一种利用跳频信号稀疏性的背景噪声估计算法.理论分析了该算法的可靠性和复杂度,并应用仿真数据和实际信号验证了算法的有效性.相比传统算法,该算法可提供相近的噪声估计结果,而计算复杂度和数据缓存大为降低.

关键词: 跳频, 噪声估计, 卫星通信, 能量检测

Abstract:

Noise estimation is the basis of the adaptive blind signal detection.It provides the thresholds of automatic detector based on the CFAR (Constant False Alarm Rate) rule.The traditional noise estimator was complicated and cannot be easily implemented in the on-line blind signal processing system for the satellite broadband frequency-hopping signal.Utilizing the sparseness of the frequency-hopping signal, a new algorithm was proposed to facilitate the noise estimation.The reliability and complexity of the algorithm was analyzed theoretically, and the effectiveness was verified through both simulations and real signal experiments.Compared to the classical algorithm, the proposed algorithm can provide similar noise estimation results, while its computational complexity and buffer consumption was significantly reduced.

Key words: noise estimation, frequency-hopping, satellite communication, energy estimation

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