Radar Reverse Modeling Based On Electronic Reconnaissance Data

As an all-weather detection device,radar plays an increasingly important role in target detection,tracking and guidance.At present,the world advanced fighters,such as F-22,F-35 and Su-35,are equipped with airborne active phased array radar,which greatly improves the detection range and achieves simultaneous multi-target tracking.Therefore,the countermeasure of airborne active phased array radar has become a very urgent and important research field.Because the performance index and working mode of airborne active phased array radar are and difficult to obtain.However,the radar must radiate the signal when it is working,and electronic reconnaissance equipment can get its relevant parameters by intercepting and analyzing the radar radiated signals.Based on the parameters obtained.Based on the parameters obtained,the radar model can be established,which provides a theoretical and experimental basis for the implementation of effective electronic countermeasures.This thesis takes the airborne fire control radar as the research object,and completes the reverse modeling of airborne fire control radar by combining radar reconnaissance and radar modeling technology.Researches and results in this thesis as follows:1.The airborne fire control radar is analyzed.The simulation of the radar airborne fire control radar's transmitter is simulated from the aspects of radar signal,antenna and beam position arrangement.The four working modes of airborne fire control radar,such as track while scan(TWS),tracking and track and search(TAS),single target tracking(STT)and velocity search(VS),are analyzed and modeled.To get the reconnaissance data we need.2.Radar signal parameters are obtained by analyzing the reconnaissance data.The pulse width and pulse repetition interval are obtained by threshold detection.Then,according to the different expressions of different modulation signals,the modulation parameters of different modulation signals are analyzed.Finally,according to the difference of bispectrum of different modulation signals,use support vector machine(SVM)to analyze the in-pulse characteristics of signals.3.The analysis of radar system parameters is completed through the previous analysis results and related reconnaissance data.Firstly,based on the system composition of the radar,the system parameters of the effective radiated power,antenna pattern,receiver sensitivity and signal processing of the radar are analyzed.Then,through further analysis of the four working modes of radar TWS,TAS,STT and VS,the results show that there are differences in signal parameters and antenna scanning characteristics between the four modes.The parameters and pulse amplitude are selected as the characteristics to identify the radar working mode through the BP neural network.Based on this,the D-S evidence theory is used to fuse the recognition results of the two features,thus improving the recognition accuracy and improving the recognition method.4.Based on the analysis of radar signal parameters and radar system parameters,the reverse modeling of radar is completed by using standardized radar modeling tools.Firstly,the standardization concept in software engineering is utilized in radar modeling,and a standardized airborne fire control radar system is established based on the analysis results.Then,based on the previous analysis results and modeling experience,the parameters of the standardized radar system are configured.And through the simulation results of the model to analysis the correctness of the model.