DocumentCode :
1988530
Title :
A new feature vector using local surrounding-line integral bispectra for identifying radio transmitters
Author :
Xu, Shuhua ; Huang, Benxiong ; Xu, Zhengguang ; Huang, Yuchun
Author_Institution :
Dept. of Electron. & Inf., Huazhong Univ. of Sci. & Technol., Wuhan
fYear :
2007
fDate :
12-15 Feb. 2007
Firstpage :
1
Lastpage :
4
Abstract :
A novel method for identifying radio transmitters with the same model and manufacturing lot is proposed in this paper. The local surrounding-line integral bispectra are selected through the Fisherpsilas class-separability discriminant measure as the main feature parameters, and they are interfused with parameters significant for classification of the received signal to form a new identification feature vector. A radial basis function(RBF) neural network is implemented to realize classification and identification for the individual transmitter utilizing the new feature vector. The selected features are evaluated using sample data of ten FM stations with the same model and manufacturing lot. It is shown that they are highly discriminative even in low SNR.
Keywords :
radial basis function networks; radio transmitters; signal classification; Fisher class separability discriminant measure; identification feature vector; radial basis function neural network; radio transmitter identification; signal classification; surrounding line integral bispectra; Character recognition; Data mining; Electromagnetic transients; Feature extraction; Frequency; Neural networks; Radio transmitters; Signal processing; Signal to noise ratio; Virtual manufacturing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on
Conference_Location :
Sharjah
Print_ISBN :
978-1-4244-0778-1
Electronic_ISBN :
978-1-4244-1779-8
Type :
conf
DOI :
10.1109/ISSPA.2007.4555493
Filename :
4555493
Link To Document :
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