电子学报 ›› 2022, Vol. 50 ›› Issue (6): 1359-1369.DOI: 10.12263/DZXB.20210916
所属专题: 电磁频谱智能+
收稿日期:
2021-07-15
修回日期:
2021-12-27
出版日期:
2022-06-25
通讯作者:
作者简介:
基金资助:
PENG Chuang, WANG Lun-wen(), HU Wei-lin
Received:
2021-07-15
Revised:
2021-12-27
Online:
2022-06-25
Published:
2022-06-25
Corresponding author:
Supported by:
摘要:
针对电磁频谱异常检测效率不高等问题,该文结合卷积神经网络(Convolutional Neural Networks,CNN)以及长短时记忆神经(Long Short-Term Memory,LSTM)网络,提出一种融合深度特征的电磁频谱异常检测算法.首先构建深度特征提取网络,该网络包含能够分级提取深度特征的两路多层CNN以及LSTM;其次通过池化、合并等操作将网络模型提取的各级深度特征进行融合,实现频谱数据预测;最后计算预测数据与真实数据的均方误差,判别频谱异常.该算法能在无监督学习的条件下,实现多种类异常状态检测的.在公开频谱数据的多个频段对算法性能进行验证,结果表明本文算法能够有效地实现电磁频谱异常检测.
中图分类号:
彭闯, 王伦文, 胡炜林. 融合深度特征的电磁频谱异常检测算法[J]. 电子学报, 2022, 50(6): 1359-1369.
Chuang PENG, Lun-wen WANG, Wei-lin HU . Spectrum Anomaly Detection Algorithm Based on the Fusion of Depth Feature[J]. Acta Electronica Sinica, 2022, 50(6): 1359-1369.
模型种类 | GSM900UL | GSM900DL | GSM1800UL | TV |
---|---|---|---|---|
本文算法 | 0.846 0 | 0.890 9 | 1 | 0.854 3 |
LSTM | 0.795 2 | 0.852 3 | 0.999 6 | 0.835 0 |
ConvLSTM | 0.714 3 | 0.745 2 | 0.993 0 | 0.787 3 |
RNN | 0.708 7 | 0.777 2 | 0.989 2 | 0.850 7 |
VAR | 0.603 3 | 0.735 4 | 0.935 3 | 0.712 6 |
CNN | 0.644 1 | 0.698 4 | 0.999 7 | 0.834 4 |
表1 各频段异常检测AUC表
模型种类 | GSM900UL | GSM900DL | GSM1800UL | TV |
---|---|---|---|---|
本文算法 | 0.846 0 | 0.890 9 | 1 | 0.854 3 |
LSTM | 0.795 2 | 0.852 3 | 0.999 6 | 0.835 0 |
ConvLSTM | 0.714 3 | 0.745 2 | 0.993 0 | 0.787 3 |
RNN | 0.708 7 | 0.777 2 | 0.989 2 | 0.850 7 |
VAR | 0.603 3 | 0.735 4 | 0.935 3 | 0.712 6 |
CNN | 0.644 1 | 0.698 4 | 0.999 7 | 0.834 4 |
模型种类 | 本文算法 | LSTM | ConvLSTM | RNN | CNN |
---|---|---|---|---|---|
参数量 | 1 144 204 | 973 568 | 32 578 176 | 862 592 | 33 473 |
表2 各模型参数量
模型种类 | 本文算法 | LSTM | ConvLSTM | RNN | CNN |
---|---|---|---|---|---|
参数量 | 1 144 204 | 973 568 | 32 578 176 | 862 592 | 33 473 |
频段 | 信干比/dB | ||||
---|---|---|---|---|---|
0 | 5 | 10 | |||
GSM900DL | 0.995 9 | 0.966 0 | 0.937 0 | 0.890 9 | 0.696 6 |
GSM1800DL | 0.971 2 | 0.902 2 | 0.819 4 | 0.703 6 | 0.635 0 |
TV | 0.959 5 | 0.943 7 | 0.888 4 | 0.854 3 | 0.750 4 |
表3 本文算法不同信干比下AUC值
频段 | 信干比/dB | ||||
---|---|---|---|---|---|
0 | 5 | 10 | |||
GSM900DL | 0.995 9 | 0.966 0 | 0.937 0 | 0.890 9 | 0.696 6 |
GSM1800DL | 0.971 2 | 0.902 2 | 0.819 4 | 0.703 6 | 0.635 0 |
TV | 0.959 5 | 0.943 7 | 0.888 4 | 0.854 3 | 0.750 4 |
频段 | 信干比/dB | ||||
---|---|---|---|---|---|
0 | 5 | 10 | |||
GSM900DL | 0.985 3 | 0.933 5 | 0.896 3 | 0.852 3 | 0.623 3 |
GSM1800DL | 0.892 1 | 0.749 7 | 0.626 9 | 0.554 9 | 0.518 7 |
TV | 0.954 9 | 0.873 6 | 0.854 3 | 0.835 0 | 0.710 3 |
表4 LSTM网络不同信干比下AUC值
频段 | 信干比/dB | ||||
---|---|---|---|---|---|
0 | 5 | 10 | |||
GSM900DL | 0.985 3 | 0.933 5 | 0.896 3 | 0.852 3 | 0.623 3 |
GSM1800DL | 0.892 1 | 0.749 7 | 0.626 9 | 0.554 9 | 0.518 7 |
TV | 0.954 9 | 0.873 6 | 0.854 3 | 0.835 0 | 0.710 3 |
频段 | 本文算法 | 卷积核为64 | 卷积核为16 | 模型不含LSTM | 4层卷积模块 | 2层卷积模块 |
---|---|---|---|---|---|---|
GSM900UL | 0.846 0 | 0.850 2 | 0.712 4 | 0.680 1 | 0.847 7 | 0.759 1 |
GSM900DL | 0.890 9 | 0.893 2 | 0.824 8 | 0.720 4 | 0.883 5 | 0.839 2 |
表5 各模型异常检测AUC值
频段 | 本文算法 | 卷积核为64 | 卷积核为16 | 模型不含LSTM | 4层卷积模块 | 2层卷积模块 |
---|---|---|---|---|---|---|
GSM900UL | 0.846 0 | 0.850 2 | 0.712 4 | 0.680 1 | 0.847 7 | 0.759 1 |
GSM900DL | 0.890 9 | 0.893 2 | 0.824 8 | 0.720 4 | 0.883 5 | 0.839 2 |
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