无线电工程

为实现卫星信号调制方式的分类，提出的高阶累积量与K最近邻算法(KNN)调制样式识别算法选取对噪声不敏感的5种高阶累积量特征参数用于信号的识别，通过KNN作为分类器对信号分类。实验结果表明，当信噪比(SNR)高于12 dB时，信号的调制方式可以被高效地识别，并且识别率趋近100%,但需要人工设计和提取特征参数。因此，提出了循环神经网络(Recurrent Neural Network, RNN)的卫星调制信号识别算法，以信号的IQ数据作为模型的输入，通过LSTM进行分时特征提取，全连接层进行分类，最终完成识别。在采样长度等于512,SNR大于4 dB时，识别率趋近100%。与KNN相比，LSTM网络的识别性能更为优越，尤其在低SNR的情况下，可以高效识别6种调制方式。

In order to classify the modulation modes of satellite signals, the proposed high-order cumulant and K-Nearest Neighbor(KNN) modulation pattern recognition algorithm selects five high-order cumulant characteristic parameters, which are insensitive to noise, to identify the signals, and uses KNN as the classifier to classify the signals.The experimental results show that when the Signal-to-Noise Ratio(SNR) is higher than 12 dB,the signal modulation method can be effectively identified, and the recognition rate is close to 100%,but it requires manual design and extraction of characteristic parameters.Therefore, a satellite modulation signal recognition algorithm based on Recurrent Neural Network(RNN) is proposed.The IQ data of signal is used as the input of the model, time-sharing feature extraction is carried out by LSTM,full-connection layer is classified, and finally the recognition is completed.When the sampling length is equal to 512 and the SNR is greater than 4 dB,the recognition rate approaches 100%.As compared with KNN,the recognition performance of LSTM network is superior, especially in the case of low SNR,it can efficiently identify six modulation modes.