تصويربرداري از هدف زميني در حال حركت توسط رادار دهانه مصنوعي مبتني بر نمايش تنك

SAR Ground Moving Target Imaging based on Sparse Representation

اﻣﺮوزه ﻋﻼﻗﻪ روزاﻓﺰوﻧﻲ ﻣﺒﻨﻲ ﺑﺮ اﺳﺘﻔﺎده از رادار دﻫﺎﻧﻪ ﻣﺼﻨﻮﻋﻲ SAR)( در ﻛﺎرﺑﺮد آﺷﻜﺎرﺳﺎزي اﻫﺪاف ﻣﺘﺤﺮك زﻣﻴﻨﻲ ) (GMTI و ﺗﺼﻮﻳﺮﺑﺮداري از اﻫﺪاف ﻣﺘﺤﺮك زﻣﻴﻨﻲ )GMTIm( ﺑﺮاي ﻫﺮ دو ﻛﺎرﺑﺮد ﻧﻈﺎﻣﻲ و ﻏﻴﺮ ﻧﻈﺎﻣﻲ وﺟﻮد دارد. از آﻧﺠﺎ ﻛﻪ SAR ﺑﺮاي ﺗﺼﻮﻳﺮﺑﺮداري از ﺻﺤﻨﻪ ﺛﺎﺑﺖ ﻃﺮاﺣﻲ ﺷﺪه اﺳﺖ، ﺗﺼﻮﻳﺮ SAR از ﻫﺪف در ﺣﺎل ﺣﺮﻛﺖ ﻣﺎت و ﺟﺎﺑﻪﺟﺎ ﻣﻲﺷﻮد. از اﻳﻦرو ﺑﺮاي ﺑﻪ دﺳﺖ آوردن ﺗﺼﻮﻳﺮ ﺑﺎ وﺿﻮح ﺑﺎﻻ در اﻳﻦ ﻣﻘﺎﻟﻪ از ﻳﻚ اﻟﮕﻮرﻳﺘﻢ ﺟﺪﻳﺪ اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ ﻛﻪ ﭼﺎرﭼﻮب آن ﻣﺒﺘﻨﻲ ﺑﺮ ﻳﺎدﮔﻴﺮي ﺑﻴﺰي ﺗﻨﻚ ) (SBLاﺳﺖ. ﺑﺮاي ارزﻳﺎﺑﻲ ﻛﻴﻔﻴﺖ ﺗﺼﺎوﻳﺮ، از ﻧﺴﺒﺖ ﻫﺪف ﺑﻪ ﻛﻼﺗﺮ )TCR ( و آﻧﺘﺮوﭘﻲ ﺷﺎﻧﻮن اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ ﻛﻪ ﻣﻌﻤﻮﻻً ﺑﺮاي ارزﻳﺎﺑﻲ ﺗﺼﻮﻳﺮ رادار دﻫﺎﻧﻪ ﻣﺼﻨﻮﻋﻲ اﺳﺘﻔﺎده ﻣﻲﺷﻮد. اﻟﮕﻮرﻳﺘﻢ ﭘﻴﺸﻨﻬﺎدي TCR ﺗﺼﻮﻳﺮ را در ﻣﻘﺎﻳﺴﻪ ﺑﺎ روش ﻫﺎي ﻣﺘﺪاول در ﺣﺪود 10dB اﻓﺰاﻳﺶ و آﻧﺘﺮوﭘﻲ ﺗﺼﻮﻳﺮ را ﺑﻪ ﻣﻘﺪار 60% ﻛﺎﻫﺶ ﻣﻲدﻫﺪ.

synthetic aperture radar (SAR) for ground moving target indication (GMTI) and imaging (GMTIm) have been gaining increasing interests for both civilian and military applications. Because SAR is generally designed for imaging a stationary scene, the SAR image of a moving target will be both displaced and smeared. More specifically, by exploiting the inherent sparsity of the moving targets in the clutter-suppressed SAR image domain, in this article. the intended SAR-GMTIm problem is solve by a sparse Bayesian perspective. The theory of CS has been successfully applied to SAR/ISAR imagery to achieve high cross-range resolution with a limited number of pulses In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic aperture radar (SAR) image assessment. The proposed algorithm shows a 10-dB higher TCR compared to the conventional algorithm.