高阶累积量和分形理论在信号调制识别中的应用研究

提出了将信号高阶累积量和分形盒维数相结合的特征提取方法。信号高阶累积量特征具有良好的抗噪性能，被广泛应用于调制识别。2ASK和BPSK的高阶累积量、以及2FSK,4FSK,8FSK的高阶累积量相等，使得只提取信号高阶累积量不足以区分信号。针对这一问题，引入信号的分形盒维数，提取信号的高阶累积量和分形盒维数构成联合特征参数，构建级联神经网络分类器，对信号进一步进行分类。对2ASK, 4ASK, BPSK, 4PSK, 2FSK, 4FSK, 16QAM七种信号进行了仿真，结果表明，该方法提取的特征参数计算复杂度低，具有较好的抗噪性能。在信噪比不低于5dB、测试样本数不少于200的条件下，正确识别率达到了85%以上。 

Research on Modulation Classification Based on High-order Cumulants and Fractal Theory

The method of feature extraction based on the combination of high-order cumulants and fractal theory is presented. Cumulants with advantage of good anti-noise performance is used to classify the modulation types widely. But the high-order cumulants of 2ASK and BPSK are equal, as well as 2FSK, 4FSK, and 8FSK, which leads to insufficiency of modulation classification. In this paper we import fractal theory to solve this problem. High-order cumulants and fractal box dimension of the received signal are extracted to build joint feature parameters. Cascade neural network classifier is structured in order to improve classification efficiency in this technique. Seven types signals as 2ASK, 4ASK, BPSK, 4PSK, 2FSK, 4FSK, 16QAM are used in simulation and the results show that the joint feature extracted by this method has lower compute complexity and better anti-noise performance. Correct classification rate is more than 85% under the condition that signal-to-noise ratio was higher than 5 dB and test samples were more than 200.