Research On Detection And Fingerprints Identification Technology Of The Short-burst Signals

Because of strong anti-interference ability and confidentiality,the short-burst signals technology is gaining more and more application and attention in the civilian and military fields.What's more,the short-burst signals have shown the superiority in hard detection and classification in the complicated electromagnetic environment.This thesis is focused on the study of detection and recognition about short-burst signals,mainly includes signal detection,preprocessing,extraction of subtle features,classification and recognition.The main works and contributions of this paper are concluded as follows:1.In the aspect of signal preprocessing,the conventional interpolated algorithms are investigated especially the cubic interpolated algorithms,which is used to satisfy the oversampling requirements of the subtle feature extraction2.Main methods of the signal present detection are studied.A novel presence detection algorithm based on maximum likelihood using preamble sequence is adopted,which can accurate the start and stop time in the transient process of burst signals.3.The algorithms of subtle feature extractions based on transmitter transient distortion and stationary distortion are proposed.In the transient distortion process,the distortion models of instantaneous amplitude and instantaneous phase are established,which completed the accurate extractions of instantaneous amplitude and instantaneous phase.Besides,the accurate extraction of modulation distortion feature is accomplished in the steady state.4.In the feature classification and recognition processing,the conceptions about SVM are recommended.A classification algorithm based on SVM is introduced,which can fulfill the classify study of the target classifier using the known data samples.Then achieve the recognition of the unknown identity information taking use of trained classifier.