TY - GEN
T1 - Specific emitter identification using fractal features based on box-counting dimension and variance dimension
AU - Wu, Longwen
AU - Zhao, Yaqin
AU - Wang, Zhao
AU - Abdalla, Fakheraldin Y.O.
AU - Ren, Guanghui
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/18
Y1 - 2018/6/18
N2 - Specific emitter identification (SEI) is a technique for distinguishing different emitters of a same type with other weak individual characteristics. Only using some traditional modulation parameters for recognition cannot distinguish different emitters with close modulation parameters. To solve the problem, new complex and high-dimensional features, which can characterize the emitters with more details, urgently need to be developed for recognition. An SEI method using fractal features based on box-counting dimension and variance dimension is presented. This paper mainly focuses on the weak individual characteristics caused by phase noise, applies fractal theory to the feature extraction, and finally establishes the recognition process using support vector machine. Numerical results show that the identification rate is generally more than 95% above 15dB of signal to noise ratio (SNR), and the real data experiment proves the practical performance of the proposed algorithm.
AB - Specific emitter identification (SEI) is a technique for distinguishing different emitters of a same type with other weak individual characteristics. Only using some traditional modulation parameters for recognition cannot distinguish different emitters with close modulation parameters. To solve the problem, new complex and high-dimensional features, which can characterize the emitters with more details, urgently need to be developed for recognition. An SEI method using fractal features based on box-counting dimension and variance dimension is presented. This paper mainly focuses on the weak individual characteristics caused by phase noise, applies fractal theory to the feature extraction, and finally establishes the recognition process using support vector machine. Numerical results show that the identification rate is generally more than 95% above 15dB of signal to noise ratio (SNR), and the real data experiment proves the practical performance of the proposed algorithm.
KW - box-counting dimension
KW - fractal features
KW - specific emitter identification
KW - support vector machine
KW - variance dimension
UR - https://www.scopus.com/pages/publications/85050132172
U2 - 10.1109/ISSPIT.2017.8388646
DO - 10.1109/ISSPIT.2017.8388646
M3 - 会议稿件
AN - SCOPUS:85050132172
T3 - 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
SP - 226
EP - 231
BT - 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Y2 - 18 December 2017 through 20 December 2017
ER -