基于环形统计量和支持向量机的CPM信号调制识别

摘要

频率成形脉冲是CPM信号调制相位的度量,也是信号解调必需的调制参数,包括脉冲形状和关联长度.本文提出一种基于环形统计量和支持向量机(Support Vector Machines,SVM)的CPM信号调制识别技术.首先将基带采样信号的瞬时频率看作环形分布的随机变量,计算瞬时频率的三角矩；然后提取其统计量作为分类特征；最后利用支持向量机,实现了不同频率成形脉冲CPM信号的识别.仿真结果表明,该方法可以实现在不同调制参数类型情况下任意脉冲成形CPM信号的识别.仿真结果中给出了包括多指数CPM信号在内的,不同频率成形脉冲CPM信号间的识别率.由于本文采用基于SVM的分类器,不依赖于信噪比条件,因此具有很高的分类性能和良好的稳健性.%Frequency Pulse Shaping is the measurement of the modulated phase of Continuous Phase Modulation ( CPM ) signals. The shape and relative length of the frequency pulse are necessary modulation parameters for the demodulation of CPM signals. In this paper, we propose a novel methodology for the recognition of CPM, which is based on circular statistics and Support Vector Machines ( SVM). The sampled instantaneous frequency is treated as a set of circular random variable. The trigonometric moments are calculated and their circular statistics are used as the classification characteristics. SVM is employed to efficiently realize the classification of different pulse shaping CPM signals. Simulation results show that this algorithm realizes the recognition of arbitrary pulse shape. In this paper, we give the recognition probability among different pulses for different modulation types, including multi-h CPM signals. The classifier of SVM doesn' t depend much on the signal-to-noise conditions; therefore it has high classification performance and good robustness.