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An Omega-K Algorithm for Translational Invariant Bistatic SAR Based on Generalized Loffeld's Bistatic Formula

机译:基于广义Loffeld双基地公式的平移不变双基地SAR Omega-K算法

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In this paper, an omega-K imaging algorithm to focus the raw data of translational invariant (TI) bistatic synthetic aperture radar (BSAR) is proposed. The method utilizes a point target reference spectrum of generalized Loffeld's bistatic formula (GLBF). Without the bistatic deformation term, GLBF is the latest development of Loffeld's bistatic formula. It is comparable in precision with the method of series reversion (MSR), but it has a much simpler form than MSR and a similar form to a monostatic case. Based on the spatial linearization of GLBF, the Stolt transformation relationship is derived. The method can consider the linear spatial variation of Doppler parameters, which is always ignored in previous publications about bistatic omega-K algorithms. This method can handle the cases of TI BSAR with high squint angles and large bistatic degrees. In addition, a compensation method for the phase error caused by the linearization is discussed. Numerical simulations and experimental data processing verify the effectiveness of the proposed method.
机译:本文提出了一种Omega-K成像算法来聚焦平移不变(TI)双基地合成孔径雷达(BSAR)的原始数据。该方法利用广义Loffeld双基地公式(GLBF)的点目标参考光谱。没有双静态变形项,GLBF是洛夫费尔德双静态公式的最新发展。它的精度可与串联反转(MSR)的方法相媲美,但它的形式比MSR简单得多,并且与单静态情况类似。基于GLBF的空间线性化,推导了Stolt变换关系。该方法可以考虑多普勒参数的线性空间变化,在以前有关双基地omega-K算法的出版物中始终忽略了这一点。该方法可以处理斜角大,双基地度大的TI BSAR情况。此外,讨论了由线性化引起的相位误差的补偿方法。数值模拟和实验数据处理验证了该方法的有效性。

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