计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 310-317.doi: 10.11896/JsJkx.190800073
陈晋音, 蒋焘, 郑海斌
CHEN Jin-yin, JIANG Tao and ZHENG Hai-bin
摘要: 无线电调制类型识别广泛应用于军民的各个领域,相比人工识别和频谱分析法等传统方法,基于深度学习的信号调制类型识别方法取得了较好性能,但仍存在识别准确率低的问题。文中提出了一种基于长短时记忆网络(LSTM)模型的信号调制类型识别方法,将深度学习分类方法与信噪比分级相结合,设计了一种基于深度学习的信噪比分级调制类型识别框架。通过准确分类高低信噪比信号,并采用不同的降噪处理来提高低信噪比信号调制类型识别的准确率。通过机器学习方法对2016.4C信号数据集进行调制类型识别的准确率为21%,通过深度学习模型对2016.4C信号数据集进行不降噪、分级降噪、全部降噪3个调制类型识别对比实验,识别准确率分别为69.82%,70.50%,66.67%,有效验证了所提方法对提高低信噪比信号调制类型识别准确率的可行性与优越性。
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