Research On SAR Deception Jamming Technology And Jamming Effect Evaluation Method

Synthetic aperture radar(SAR),as a high-resolution imaging remote sensing technology,is widely used in military reconnaissance and civilian fields around the world due to its advantages of all-weather,all-weather operation,and strong penetration.With the continuous deepening of the impact of artificial intelligence technology on various SAR technologies,in order to effectively protect our important areas from illegal detection and reconnaissance by the enemy,and reduce the ability of the enemy SAR system to reconnaissance information,the research on real-time,intelligent,high-quality SAR deception jamming technology,as well as scientific,reliable,and accurate new methods for evaluating the effectiveness of SAR deception jamming,has important theoretical significance and military value.This thesis studies SAR deception jamming technology and jamming effectiveness evaluation methods,and the main work includes the following three aspects:1.SAR deception jamming methods based on image inversion is researched and analyzed.On the basis of SAR echo signal model and CS(Chirp Scaling)imaging algorithm theory,this paper first studies the method of SAR echo inversion theory based on ICS(Inverse Chirp Scaling).Then,the mechanism of SAR deception jamming is studied,and the theoretical feasibility of the method is verified by point target simulation and real scene simulation in data source.Finally,the traditional pixel-based SAR deception jamming evaluation method is studied and analyzed.It lays a foundation for the new method of deception jamming and the new method of evaluating the effect of deception jamming.2.Existing methods for generating synthetic aperture radar(SAR)deception jamming signals have slow speed,low imaging quality,and insufficient intelligence in complex electromagnetic environments.This thesis proposes a deep learning-based SAR deception jamming signal generation method based on deep echo inversion Unet(DEIUnet).This method has high speed and provides high image quality of the jamming signal.A SN block(Swin-Ne Xt block)is proposed to combine local and non-local information in the image and echo data.The Unet structure consists of SN blocks,and a residual connection is used as the jump connection to fuse the multi-scale feature information from the echo and image data.Pixel Shuffle is utilized for up-sampling to generate high-quality echo data.The experimental results on MSTAR and Sentinel-1 data sets verify the effectiveness and superiority of DEIUnet for echo inversion.The imaging results of the SAR deception jamming signal generated by DEIUnet on an MSTAR scene confirm the effectiveness of the proposed method.3.Existing methods for evaluating the effectiveness of SAR deception jamming have low reliability and accuracy in the complex countermeasure environment.This thesis proposes a new SAR deception jamming effectiveness evaluation method based on Visual Attention Network(VAN).Firstly,through theoretical research and analysis of the influence of reconnaissance errors on the existence of jamming effect in the process of generating jamming signals based on inversion method,SAR jamming error data sets containing different error types and degrees are constructed and generated.Secondly,a VAN based SAR jamming effect evaluation method is proposed to evaluate the difference of SAR jamming effect caused by parameter reconnaissance error scientifically,reliably and accurately.Finally,the simulation results on the generated data set validate the effectiveness of the proposed method.Compared with the evaluation results of the pixel-based SAR spoof-jamming effect evaluation method in this data set,it is further proved that the VAN based spoof-jamming effect evaluation method has better reliability and accuracy.