一种混合模式的神经网络自动调制识别器
doi: 10.3724/SP.J.1146.2007.00515
An Automatic Modulation Recognizer Using Neural Networks Based on the Hybrid Mode
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摘要: 数字信号自动调制识别(AMR)有基于决策论和统计模式两种方法,该文提出一种将两者相结合的自动调制识别系统,利用提取决策论特征向量集和统计特征向量集相结合的特征参数,使用带动量项的自适应权重的BP神经网络对MASK,MFSK,MPSK,MQAM等4类信号进行分类识别。当信噪比在0-10dB,在估计载频与实际载频相差0-100Hz的情况下正确识别率仍高达97%以上,实验证明这种分类识别方法的鲁棒性和实用性。Abstract: On automatic modulation there are two approaches, decision-theoretic and statistical pattem. An automatic modulation recognition system to recognize four digital signal classes as MASK, MFSK, MPSK, MQAM is proposed in this paper, which using decision-theoretic based feature set addition to statistical pattem based feature set with momentum auto-adapted weight BP neural network. Performance is generally good when Signal to Noise Ratios (SNR) in 0-10dB, and the estimated carrier frequency differs from the actual carrier frequency of 0-100Hz, simulations show the results even larger than 97%, that confirm the robustness and practicability of this recognition method.
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