Key parameter estimation for radar rotating object imaging with multi-aspect observations

查看全文 作　　者：YE ChunMao, XU Jia , PENG YingNing & WANG XiuTan Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

出　　处：《Science China(Information Sciences)》索引2010年第53卷第8期,共12页

基　　金：supported by the National Natural Science Foundation of China (Grant No. 60971087);China Ministry Research Foundation (Grant No. 9140A07011810JW01);the Aerospace Innovation Foundation (Grant No. CASC200904);China Aviation Science Foundation (Grant No. 20080158001) 摘　　要：Rotating object model is commonly used for imaging analysis in high resolution radars such as the inverse synthetic aperture radar (ISAR). For a rotating object, it is known that multi-aspect observations can improve cross-range resolution with the known imaging geometry. For the non-cooperative rotating object with unknown imaging geometry, this paper proposes an integrated scheme to estimate the key parameters, e.g., the rotating velocity and the aspect angle difference between every two observations. Furthermore, convolution back-projection (CBP) method is applied to provide fused imaging result with improved resolution. Also, the accuracy of the ultimate parameter estimation is analyzed, which is strongly related with several important factors like position extraction error of scattering centers and so on. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed method. 关键词：旋转速度成像雷达多层面参数估计逆合成孔径雷达几何对象距离分辨率卷积反投影

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Key parameter estimation for radar rotating object imaging with multi-aspect observations

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