DocumentCode :
1797844
Title :
Individual radiation source identification based on fractal box dimension
Author :
Jingchao Li ; Yulong Ying
Author_Institution :
Coll. of Electron. Inf., Shanghai Dianji Univ., Shanghai, China
fYear :
2014
fDate :
15-17 Nov. 2014
Firstpage :
676
Lastpage :
681
Abstract :
Nowadays, it is difficult to identify the individual radiation source under low SNR environment. To this problem, the paper proposed a new fractal box dimension based algorithm, to calculate the fractal box dimension of different communication individual radio signals as the subtle characteristics. Basing on the traditional fractal box dimension, the proposed algorithm calculated the derivations of different reconstructing phase space points, and getting the fractal box dimension of the communications signals under different reconstructing conditions, which constitute a feature vector, to realize the purpose of extracting the subtle characteristics of individual radiation source more exactly. Finally, neural network was used to process and classify the fractal box dimension vector features, in order to achieve the purpose of classifying and recognizing different communication radiation source under complex environment.
Keywords :
feature extraction; identification; neural nets; radiocommunication; signal reconstruction; telecommunication computing; communication radiation source; communication signals; feature vector; fractal box dimension based algorithm; individual radiation source identification; low SNR environment; neural network; phase space point reconstruction; radio signals; Feature extraction; Fitting; Fractals; Signal to noise ratio; Space stations; Support vector machine classification; Vectors; Communication radio identification; Feature extraction; Fractal box dimension; Subtle features;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems and Informatics (ICSAI), 2014 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4799-5457-5
Type :
conf
DOI :
10.1109/ICSAI.2014.7009371
Filename :
7009371
Link To Document :
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