Intra-pulse Feature Analysis And Recognition Of Radar Emitter

With the rapid development of modern electronic information technology,new radar systems are emerging constantly.How to carry out feature extraction and target recognition from radar received signals has become an important research content of electronic reconnaissance.In this paper,the extraction and identification methods of radar emitter's in-vein features are studied.The main work and achievements include:1.In this paper,the mechanism of two kinds of radar inpulse-modulation modes is introduced firstly,and the main reasons that affect the fingerprint characteristics of radar emiter are analyzed.In this paper,the two main factors of frequency offset and parasitic phase modulation are used as the basis and premise for the modeling of fingerprint model because of their strong testability and stability.2.Different methods of feature extraction are usually adopted for different modulation modes.In this paper,a method of intentional modulation feature extraction based on time-frequency analysis is studied.This method uses the spectrum and time-frequency distribution difference of different modulated signals and decision tree to identify them.Experiments show that the modulation recognition method used in this paper can represent the time-frequency information of different modulation modes,and the average identification accuracy of samples can reach more than 96% at 6d B.3.In order to reflect the essential differences of individual radiation sources,frequency stability is taken as the fingerprint feature in this paper,and two improved accurate frequency measurement methods are proposed for CW signal and LFM signal.In order to improve the error caused by the spectral line spacing of the periodic graph method,a precise estimation method of CZT frequency is applied in this paper to realize the accurate estimation of CW signal frequency.This method improves the resolution by subdividing the spectrum around the rough estimation and reduces the sampling interval error.For linear frequency modulated signal(LFM),an improved intelligent particle swarm optimization method is proposed to complete the extraction of frequency features,which ensures the accuracy and greatly improves the efficiency of local search maximum likelihood method.In addition,the CRLB theoretical performance bounds of fingerprint model parameter estimation are derived.Experiments show that the precise estimation method of frequency parameters used in this paper can approach the CRLB boundary,and the identification accuracy of radar emitter reaches more than 90% at7 d B.4.In order to fully describe the nonlinear fingerprint features of individual radiation sources and further reduce the difficulty of traditional artificial feature extraction,a method of individual recognition based on one-dimensional residual network and feature enhancement is studied in this paper.The method takes advantage of the anti-noise ability of the bispectrum,and extracts three feature vectors of the bispectrum: singular vector,diagonal slice and SIB vector.Combined with the received one-dimensional time-series signals,the method is sent into the designed residual network for training and learning.Experimental results show that the proposed method can effectively use the limited information and achieve a higher recognition accuracy than the traditional method using only time series signals or bispectral features.