SAR Active Deception Jamming Template Generation And Effectiveness Evaluation

Synthetic Aperture Radar(SAR)has the advantages of all-time,all-weather,long range and strong penetration ability.It is widely used in civil areas such as topographic mapping,resource exploration,disaster monitoring and military fields such as battlefield reconnaissance,situation surveillance and precise guidance.With the wide application of SAR technology,the research of SAR countermeasure has an urgent application demand.The SAR active jammer destroys the SAR imaging effect by transmitting jamming to the other SAR,thus reducing the ability of the other SAR system to acquire the information of the observation area.According to the implementation mode,the active jamming of SAR can be divided into active suppression jamming and active deception jamming.Compared with the direct transmission of high-power suppression jamming,deception jamming modulates and forwards the intercepted or regenerated radar echoes,resulting in false scenes in SAR images,making it difficult for the other party to correctly judge the battlefield situation and form correct decisions.This method has the advantages of concealment and low jamming power.However,active deception jamming requires high reconnaissance accuracy.The existing methods of template generation for SAR deception jamming are difficult to meet the needs of high similarity of template texture and diversity of generated results.At the same time,the existing methods for evaluating SAR deception jamming have the problems of single item and poor universality.Therefore,the research of SAR deception jamming template generation and effect evaluation method has important theoretical significance and application value.Starting from the mechanism of SAR imaging and active deception jamming,this paper will study how to expand the scope of deception jamming,improve the fidelity of deception scene and comprehensive evaluation of deception jamming effectiveness.The main work and innovations of this paper are as follows:1.Firstly,the principle of SAR imaging is introduced,the principle of SAR high resolution and the method of SAR signal processing are analyzed,and the classical SAR imaging algorithm and Range Doppler algorithm are introduced and simulated.Secondly,based on the principle of SAR imaging,the basic methods of deception jamming are introduced,and the geometric model of deception jamming is established and simulated.Then,aiming at the problem of real-time and effective range of deception jamming,a fast deception jamming method in large scenes is studied.The real-time and effective range of deception jamming are improved to a certain extent by using two-step generation method and piecewise compensation method,and the algorithm is verified by simulation in large scenarios.2.Aiming at the problem of limited fidelity of false scenes,the theory of generative adversarial networks is innovatively introduced into the research of deception jamming template generation algorithm.Firstly,the basic model of generative adversarial networks is established and its principle is analyzed,and its improved network model is further studied.Based on the analysis of existing network models,a new generative adversarial networks model suitable for the generation of deception and jamming templates is established.At the same time,SAR image data set of SAR scene is established by pre-processing SAR image of given scene.On this basis,the network model is trained by in-depth learning method,and the result of interference template generation is obtained.Finally,the traditional deception jamming template generation method based on template library and texture synthesis is briefly analyzed,and the texture synthesis method based on block stitching is introduced and simulated.The advantages of the proposed method are verified by comparing the two methods for generating deception jamming templates.3.Aiming at the problem that the single item and poor universality of existing methods for evaluating active deception jamming of SAR,a new method for evaluating active deception jamming of SAR based on convolution neural network is proposed.Because of the scarcity of deception jamming images in SAR,this part firstly analyses the influence of track error on the modulation function of deception jamming and the quality of deception jamming images when the track estimation accuracy of Deception Jammer is limited,and establishes a sample set of deception jamming images based on track error.The sample set will be used as a sample for validation of follow-up indicators and training of interference assessment network.Secondly,the SAR deception jamming evaluation method based on image quality index is studied and analyzed,and the processing results are taken as the input vector set of the depth neural network to obtain the final comprehensive evaluation results of the jamming effect.Finally,a deception jamming effect evaluation method based on convolution neural network is proposed.The validity of the proposed method is verified by the simulation experiment of the deception jamming image sample set of track error.