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应用科学学报 ›› 2011, Vol. 29 ›› Issue (6): 565-570.doi: 10.3969/j.issn.0255-8297.2011.06.003

• 信号与信息处理 • 上一篇    下一篇

采用分数低阶循环谱相干系数的调制识别

赵春晖， 杨伟超， 杜宇   

  1. 哈尔滨工程大学信息与通信工程学院，哈尔滨150001
  • 收稿日期:2011-04-11 修回日期:2011-06-29 出版日期:2011-11-30 发布日期:2011-11-29
  • 通信作者: 赵春晖，教授，博导，研究方向：图像及非线性信号处理，E-mail: zhaochunhui@hrbeu.edu.cn
  • 作者简介:赵春晖，教授，博导，研究方向：图像及非线性信号处理，E-mail: zhaochunhui@hrbeu.edu.cn
  • 基金资助:

    国家自然科学基金(No.61077079)；高等学校博士学科点专项科研基金(No.20102304110013)；哈尔滨市优秀学科带头人基金(No.2009RFXXG034)资助

Modulation Recognition Using Fractional Low-Order Cyclic Spectrum Coherence Coefficient

ZHAO Chun-hui, YANG Wei-chao, DU Yu   

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  • Received:2011-04-11 Revised:2011-06-29 Online:2011-11-30 Published:2011-11-29
  • About author:赵春晖，教授，博导，研究方向：图像及非线性信号处理，E-mail: zhaochunhui@hrbeu.edu.cn

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1588

被引次数

17

摘要：

摘要： Alpha稳定分布噪声导致二阶循环谱相干系数失效，使相应的通信信号调制识别算法退化. 针对这个问题，提出了基于分数低阶循环谱相干系数的识别算法. 文中给出了分数低阶循环谱相干系数的相关理论，分析了通信信号的分数低阶循环谱相干系数，在此基础上提取谱相干系数循环频率域特征作为识别特征参数. 用BP神经网络为分类器，实现了通信信号调制方法识别. 仿真结果表明，在Alpha稳定分布噪声下，该识别算法性能优于基于二阶循环谱相干系数的方法. 在高斯噪声条件下，两种识别算法性能相当.

关键词: 调制识别, Alpha稳定分布噪声, 分数低阶循环谱, 谱相干系数

Abstract:

Noise with alpha stable distribution leads to loss of efficacy of the second-order cyclic spectrum coherence coefficient, and degrades related algorithms for communication signal modulation recognition. A recognition algorithm based on fractional low-order cyclic spectrum coherence coefficient is proposed to solve this problem. The related theory of fractional low-order cyclic spectrum coherence coefficient is first introduced.
Fractional low-order cyclic spectrum coherence coefficients of communication signals are analyzed. Based on the analysis, the algorithm extracts the cyclic frequency profile of spectrum coherence coefficient as the recognition characteristic parameter, and uses a BP neural network as a classifier to achieve communication signal modulation recognition. Simulation results show that, in an alpha stable distribution noise environment, the performance of the proposed algorithm is superior to that based on second-order cyclic spectrum coherence coefficient. These two algorithms have the same performance in Gaussian noise.

Key words:  modulation recognition, alpha stable distribution noise, fractional low-order cyclic spectrum, spectrum coherence coefficient

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