Moving targets in synthetic aperture radar (SAR) imagery often yield signatures that are smeared beyond recognition. One mitigation strategy is to attempt to refocus such smears as if the corresponding targets were stationary. This problem is especially difficult for cases in which the target executes time-varying rotation rates for roll, pitch and yaw, as well as for simultaneous non-linear target translational motion. The current analysis develops an arbitrary rigid object motion autofocus (AROMA) for performing automatic refocus of such targets having non-uniform rotation and translation profiles. AROMA uses a maximum likelihood signal-theoretic analysis of the physical rotation angles and translation distances arising from the unknown rigid object motions in order to generate refocused target imagery. The efficacy of this methodology is demonstrated via the injection of a simulated rotating and translating target comprised of multiple scattering centres into a cluttered background of measured Ku-band SAR data. Furthermore, the application of AROMA to an input defocused smear corresponding to a ‘target-of-opportunity’ vehicle in measured Ka-band SAR imagery yields significant improvement in the target focus, thus further validating the underlying theoretical analysis developed herein.

中文翻译：

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非均匀目标旋转和平移的任意刚性物体运动自动聚焦理论

合成孔径雷达（SAR）影像中的移动目标通常会产生难以识别的污迹。一种缓解策略是尝试将这些涂片重新聚焦，好像相应的目标是静止的一样。对于目标执行滚动，俯仰和偏航的时变旋转速率以及同时进行非线性目标平移运动的情况，此问题尤其困难。当前的分析开发了一种任意的刚性物体运动自动聚焦（AROMA），用于对具有不均匀旋转和平移轮廓的目标执行自动重新聚焦。为了生成重新聚焦的目标图像，AROMA对物理旋转角度和由未知刚性物体运动引起的平移距离进行了最大似然信号理论分析。通过将由多个散射中心组成的模拟旋转和平移目标注入到测量的Ku波段SAR数据的杂乱背景中，证明了该方法的有效性。此外，在测量的Ka波段SAR图像中，将AROMA应用于与“机会目标”车辆相对应的输入散焦拖尾涂片，可以大大改善目标焦点，从而进一步验证本文开发的基础理论分析。