基于模糊熵的连续相位调制识别算法

针对不同调制参数的多调制指数(Multi-h)连续相位调制(CPM)信号间识别问题，提出一种基于模糊熵的调制识别算法。模糊熵理论摒弃了近似熵中距离与数目的二值化相似性判断，提出利用隶属度函数判断相似性，可以更精确地描述时间序列的复杂度。算法分离接收信号的同相和正交分量并分别求其模糊熵，将求取的模糊熵作为分类特征送入支持向量机(SVM)进行分类，完成不同Multi-h CPM信号的调制识别。仿真实验结果表明，该算法在信噪比大于6 dB时，对不同调制指数集合的全响应矩形成形Multi-h CPM信号可以实现100%识别，且仅需较少符号数即可实现调制识别。

Continuous phase modulation recognition algorithm based on fuzzy entropy

To address the recognition challenge of Multi-h Continuous Phase Modulation (Multi-h CPM) signals with varying modulation parameters, this paper proposes a modulation recognition algorithm grounded in fuzzy entropy theory. This theory transcends the binary approach of distance and count-based similarity in approximate entropy, opting for a membership function to assess similarity and more accurately reflect the complexity of time series. The algorithm separates and calculates the fuzzy entropy of the in-phase and quadrature components of the received signal, utilizing these values as classification features for a Support Vector Machine(SVM). Experiments demonstrate that the algorithm achieves 100% recognition accuracy for full-response rectangular shaped Multi-h CPM signals across various modulation index sets at signal-to-noise ratios above 6 dB, and enables modulation recognition with a minimal number of symbols.