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Focusing Bistatic SAR Data Using the Nonlinear Chirp Scaling Algorithm

机译:使用非线性线性调频缩放算法聚焦双站SAR数据

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Bistatic synthetic aperture radar data are more challenging to process than the common monostatic counterparts because the flight geometry is more complicated and the data are usually nonstationary. Whereas time-domain algorithms can handle general bistatic cases, they are very inefficient; therefore, frequency-domain methods are preferred. Several frequency-domain monostatic algorithms have been modified to handle a limited number of bistatic cases, but a general algorithm is sought, which can handle cases such as nonequal platform velocities, nonparallel flight tracks, and high squints. In this paper, we modify the nonlinear chirp scaling (NLCS) algorithm to handle a general case of bistatic data. The key is to use a linear range cell migration correction to reduce the range-azimuth coupling, an NLCS to precondition the data for azimuth compression, and a series expansion to obtain an accurate form of the signal spectrum. The azimuth nonstationarity is handled through the use of invariance regions. Simulations have shown that the modified NLCS algorithm can handle data with more complicated bistatic geometries than the previous algorithms.
机译:与普通的单基地同行相比,双基地合成孔径雷达数据的处理更具挑战性,因为飞行几何形状更加复杂并且数据通常是不稳定的。时域算法可以处理一般的双基地情况,但效率很低;因此,频域方法是首选。已对几种频域单基地算法进行了修改,以处理有限数量的双基地情况,但正在寻求一种通用算法,该算法可以处理诸如平台速度不相等,飞行轨迹不平行以及斜视率很高的情况。在本文中,我们修改了非线性线性调频标度(NLCS)算法,以处理双基地数据的一般情况。关键是使用线性距离像元迁移校正来减少距离-方位角耦合,使用NLCS预处理数据以进行方位角压缩,以及使用串联扩展来获得准确形式的信号频谱。通过使用不变区域来处理方位角的非平稳性。仿真表明,与以前的算法相比,改进的NLCS算法可以处理具有更复杂的双静态几何形状的数据。

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