Analysis On Influence Of Image Enhancement Algorithm To SAR Jamming

With the improvement of Synthetic Aperture Radar(SAR)imaging resolution imaging and the continuous deepening of SAR image interpretation,the application of SAR image enhancement has gradually attracted attention.Currently,the interference technology for SAR presents a diversified trend.Their purpose is to destroy the detection of the target scene by SAR,so that the radar system cannot obtain the accurate information of the target image.Image enhancement algorithm will inevitably affect the interference effect.In order to ensure the effectiveness of SAR interference,it is necessary to analyze the effect of image enhancement algorithm on the interference effect.Firstly,the application background and the development of SAR countermeasures is reviewed.Jamming pattern and image enhancement method on SAR are introduced.In the area of SAR interference technology,the development of active noise interference is more mature.Active noise interference is easy to implement,easy to operate,and widely used in the actual combat process.It is an important type of interference.For image enhancement methods,there are hotspots of wavelet transform based on image transform domain processing,robust principal component analysis(RPCA)methods based on compressed sensing and sparse expression theory.Secondly,the principle of radar imaging is briefly discussed,Starting from the geometric scene,the distance model of SAR imaging was established and the RD processing algorithm was introduced.and four active noise jamming models in the imaging process are established,including frequency modulation noise jamming,noise amplitude modulation jamming,noise frequency modulation jamming,and noise phase modulation jamming.The influencing factors of SAR image quality and the evaluation method of jamming effectiveness are also described.Thirdly,the wavelet transform denoising method is discussed in detail,and the global threshold and layered threshold denoising methods are simulated.The results show that the global threshold can filter out the noise while discarding some of the details of the image,making the image difficult to discern.The layered threshold preserves the image feature,but also can suppress noise jamming and reduce the difficulty of image interpretation.Finally,the PCA method is briefly introduced,and then it transitions to RPCA.Detailed mathematical derivation and argumentation of RPCA is carried out,and the basic idea of RPCA for SAR image denoising is introduced.This method is applied to noise interference processing.The conclusion was reached through experimental simulation.In terms of image indicators,RPCA has similar effects on suppressing four types of active noise interference.From the performance analysis of RPCA,the number of calculations dealing with noise AM interference and noise PM interference is the least,followed by RF noise interference,and finally noise FM interference.Therefore,in applications with high real-time requirements,RPCA can be used to handle noise amplitude modulation interference and noise phase modulation interference.In this thesis,the study of image enhancement interference in SAR imaging is still one-sided.First of all,from the SAR system,without using real SAR system parameters,the simulation of the scene model is simplified;only four of the more typical noise interferences are selected in the interference method;the only effect from the denoising is in the wavelet transform method.It is demonstrated that for the feature extraction of scene objects,no further exploration has been done on edge detection;in the RPCA denoising process,the ADMM algorithm for model solving can be considered using distributed computing to further improve the efficiency.