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.

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.