Research On Radar Emitter Fingerprint Feature Extraction And Identification Technology

Recognizing radar platforms by receiving radar signals is a very important issue in modern electronic warfare.The intra-pulse modulation of the radar emitter signal can be divided into intentional modulation on pulse and unintentional modulation on pulse.The feature of unintentional modulation on pulse is also called the fingerprint feature.In order to realize the identification of individual radar emitter,the extraction of fingerprint features of the radar emitter signal becomes an important task for the radar reconnaissance receiver.The paper conducts research on the following aspects:The influence of various components of the radar transmitter on the radar emitter signal and the mechanism of the fingerprint feature of the radar emitter are studied.Then the simulation signal model is established accordingly,which allows the simulation signal correctly reflect the signal characteristics including fingerprint features,and serve as a basis for subsequent the fingerprint feature extraction and recognition research of the radar emitter.The basic composition of the radar emitter identification system is analyzed.The radar emitter signals are accurately classified and identified by combing the intentional modulation features and unintentional modulation features of radar signal.For the low recognition rate of radar signal in complex electromagnetic environment,the time-frequency analysis of radially Gaussian kernel is applied to radar signals.Through singular value decomposition of time-frequency distribution,we can extract its singular values as feature parameters for radar signal recognition.The simulation results show that this method improves the recognition rate of radar signal under low SNR.In order to solve the difficulty of extracting fingerprint features of individual radar emitter in the process of identifying specific radar emitter,a method of fingerprint feature extraction is analyzed,which is based on the front edge waveform of pulse envelope of radar signal.Then we propose the method of fingerprint feature extraction based on variational mode decomposition of radar signal.This method divides the unintentional modulation of the radar pulse amplitude envelope into different modes based on VMD,and calculates the center frequencies of these modes as fingerprint feature parameters to classify radar emitters.The Clustering experiments show that the fingerprint feature extraction method based on VMD has a good classification effect on different radar emitter signals in the case of high SNR.In the process of fingerprint identification of radar signal,fewer labeled samples of the actual sampled signal is a common and challenging problem.A novel algorithm named improved semi-supervised active learning is proposed for fingerprint identification of radar emitter signals,which is based on pseudo-labels verification procedure.The proposed algorithm enables a collaborative labeling procedure by combing active learning and semi-supervised learning to reduce the number of labeled samples required in the learning model effectively.The simulation experiments show that the improved recognition algorithm improves the final classification performance and realizes the high probability of radar signal recognition under the condition of less number of labeled samples.