Research On Specific Emitter Identification Method Based On Wavelet And Chaos Theory

In electronic reconnaissance technology,correctly processing and analyzing enemy signals and classifying and identifying them is a very meaningful but challenging link,It will directly affect the control of the battlefield situation in the later period and the development of confrontation strategies.The enemy signals intercepted in a specific time and space are a mixture of different signals,after the classic signal processing identification,they may still show the same signal modulation type and modulation parameters,similar spectrum characteristics,etc.,It is a meaningful issue to correctly identify the signal transmission equipment,to distinguish the characteristics of ordinary signals,and to explore its mechanism and characteristics in depth to identify individual radiation sources and obtain battlefield information.The signal identification of a specific radiation source only focuses on the unintentional modulation characteristics of the detection signal.The components of the signal transmitter are affected by the temperature and humidity environment and long-term work during operation.There will be some specific changes,and these changes will be reflected in various forms in various transmitted signals,the subtle signal characteristics are exactly the difference of these devices.Based on the measured data,this paper extracts a variety of features for individual recognition by studying the wavelet frequency domain and phase space domain.The specific research content is as follows:Firstly,combining the structure of the radiation source transmitter system from the oscillator and the power,the mechanism of unconscious modulation with fixed discrimination between different individuals is studied and the signal simulation is completed.Preprocess the measured data and study its basic characteristics such as time domain,frequency spectrum,envelope,envelope amplitude distribution,mean and variance.Based on the fingerprint mechanism,the frequency amplification curve of the measured data is studied,and the feasibility of using the curve to extract features is analyzed.On this basis,an adaptive energy window and a specific feature sequence are proposed according to the effect of the nonlinearity of the pow er amplifier on the frequency.Secondly,the skewness and kurtosis characteristics were studied on the basis of the normalized envelope amplitude distribution of the measured data,and three different sequences were used to complete the feature extraction,including full pulses,full frequency bands within the energy window,and feature sequences.Individual characteristics.The skewness feature based on wavelet is further studied and dimensionality reduction processing is carried out under noisy sequences.In addition,the wavelet packet decomposition with better performance is selected.For different input methods,the corresponding component entropy is studied as another feature of individual identification of the radiation source.For this energy feature,Relief F and K-S feature dimensionality reduction algorithms are selected in this paper.Study the skewness,kurtosis and wavelet entropy characteristics of various dimensions of various input methods,and classify and recognize them in th e KNN classification algorithm.Finally,based on the fact that the differences between the individuals of the radiation sources come from the nonlinear effects of the system devices,chaotic theory is used to process the measured data of this subject and extract features to complete the individual identification process.The measured data is chaotic and the embedding time delay and dimensional parameters are obtained using a classic algorithm.Based on this,phase space reconstruction is performed and corresponding features are extracted to complete individual identification.