Analysis And Identification Of Fine Features Of IFF Radiation Source Signals

Individual identification of radiation source is always a hot topic in the field of electronic warfare.In recent years,the application of IFF system in modern battlefield is becoming mature.In order to achieve effective electronic countermeasures against the enemy IFF system and obtain the initiative of the battlefield,an important technical support for electronic countermeasures reconnaissance intelligence is required,which makes the subtle feature analysis of IFF radiation source signals and individual identification research of IFF radiation source are becoming more and more important.The subtle features of IFF radiation source signals are mainly caused by the difference of internal components of the transmitter,and the subtle features of different individuals have stable differences,which provides an important basis for individual identification research.Starting from the stability of frequency source,this paper proposes an improved DFT frequency offset estimation algorithm based on Zoom-FFT.Based on the DFT frequency estimation algorithm,the advantage of Zoom-FFT subdivision frequency is fully utilized to obtain a fine estimation frequency.The carrier frequency offset value is obtained by comparison with the nominal frequency.In this paper,a feature extraction method based on time-frequency and texture analysis and a feature extraction method based on time-frequency analysis and fast sample entropy are proposed based on the nonlinear characteristics of power amplifier.Through the time-frequency analysis method,the rich details of IFF radiation source signals are analyzed,and the multi-dimensional effective features are extracted as the subtle features of signals with the help of the image texture analysis technology.In addition,the subtle differences of signals expressed by the quick sample entropy,which is more efficient and noise-resistant,are used to improve the recognition effect and efficiency.In addition,this paper aims to improve the whale algorithm,whitch is a new intelligent optimization algorithm,to improve the classification and recognition ability of SVM and provide a strong guarantee for individual identification.The methods proposed in this paper are verified by simulation experiments,which shows the feasibility and effectiveness of the methods.