Individual Transmitter Fingerprint Identification Based on Bispectra and Combination Kernel Function PCA
|Course||Electronics and Communication Engineering|
|Keywords||transmitter fingerprint identification Signal bispectra feature Combination kernel function PCA Conditionally positive definitekernel Support vecter machine|
As a result of the subtle differences in the hardware of each communication device, some individual characteristics which differ from other devices exist in the emitted signal. Communication transmitter fingerprint identification, which research the individual characteristics carried by the communication signal to identify the communication equipment, and then track the transmitter, privoids the thread of the enemy communication system structure, strategic intent and the formulation of our plan of action, and have important strategic sense in the communication countermeasure field. The primary work is summarized as follows:1. The bispectrum is used to extract feature of the communication signal. Because of the direct use of bispectra as characteristics may cause to the greatly computational quantity, the local integral bispectrum is used for the feature extraction. The advantages and disadvantages of several local bispectrum feature extraction method is reseached and compared in the experiment, and finally the square integral bispectra is choosed as the method of the bispectrum feature extraction.2. In order to solve the problems that the dimension of the signal bispectrum features vector is too high, and the existing kernel principal component analysis may overlook the local features of the signal easily, an algorithm based on combination Kernel Function Principle Component Analysis for the reduction of the dimension of the communication signal bispectrum features vecter is presented, the algorithm which introduces Conditionally positive definite kernel function, and combines with a global kernel function, looks after both the global and local features of the data, and gets the feature vecter with the suitable dimension.3. SVM is used to construct the communicatin transmitter classifier. After the dimension reduction, the signal bispectrum features vector is used to train SVM classifier and the transmitter fingerprint identification classifier is constructed.