Research On SAR Imaging Algorithm In High Squint Angle And Sidelobe Control Technique

As a high resolution imaging radar, synthetic aperture radar(SAR) is one of the most important imaging tools and plays an increasing significant role in the modern remote-sensing application. It can produce high quality and high resolution SAR image,and it also has been widely used in the national defense and people’s daily life. However,with the development of the modern military weapons and the improvement of the quality of people’s lives, SAR imaging and image processing technology have been a hot topic for scholars. So far, while many effective SAR imaging and image processing methods have been put forward, in practice, due to the diversification of target strength and the complexity of scene, SAR imaging and image processing technology still faces formidable challenges.In this paper, we have a detailed analysis of the imaging scene including lots of strong points. My research achievements are showed below:1.A modified polar format algorithm(MPFA) based on rotating coordinate system is presented, which can applyed to high squint angle situation. It based on the classic polar format algorithm(PFA). We put a rotary squint angle to the SAR imaging geometry coordinate system, recalculate the position of the SAR in the new coordinate system,and then get the distance between the radar and target points. By rotation of the coordinate system, the geometry model is transformed into the positive side looking case, this method not only overcomes the defects of PFA applying to non-squint case,but also make the imaging targets in large squint still be the non-skewed cross. It establishes a important foundation for the sidelobes control.2.A constrained modulus spatially variant apodization(CMo SVA) is proposed. This method is based on the classic spatially variant apodization(SVA) model, and we calculate the weighted factor by using modulus filtering method to minimize the energy of each pixel. Then, a constrained factord is introduced. Comparing the weighting factor with constraints factor, we achieve different comparison results, and then depending on the comparison results, we get the different output values of each pixel. After applying this new method in the SAR image, the sidelobles can be reduced to a low level and also the width of the mainlobes is becoming more narrower, with the resolution of the imageimproving. However, this new approach also bring some defects, because when the sidelobe is controlled, the energy of the whole SAR image is reduced, too. That is to say,the SAR image is become dimmer than the original one. Thus, SAR image background filtering technology is added in the CMo SVA, which was developed to solve defects caused by sidelobles reducing. First, we extract the background of the original SAR image, and then the dark background of the image treated by using sidelobe reduce technology mentioned above is replaced by corresponding background of the original image. After applying the CMo SVA we proposed, a high quality and high resolution SAR image is obtained finally.This work is surpported by national natural science foundation of China(No. 61173092),new century excellent talents to surpport plan(No.66ZY110) and shaanxi province science and technology research and development projects(No. 2013KJXX-64)..