Video synthetic-aperture radar (ViSAR) can obtain high-resolution images of a region of interest at a high frame rate. This feature of ViSAR is helpful for real-time detection and tracking of moving targets. Moving-target tracking using ViSAR images is a typical dim-target-tracking problem. In the context of this article, dim targets correspond to the shadows of the moving vehicles cast onto the stationary background scene, which appear at lower gray levels compared with the background clutter. To detect and track multiple slowly maneuvering targets in the ViSAR imagery, we propose a novel algorithm, the expanding and shrinking strategy-based particle filter/dynamic programming-based track-before-detect (ES-TBD) algorithm. To the best of our knowledge, our work represents the first algorithm to deal with the ViSAR-detection and tracking problem using the TBD method. Furthermore, to detect and track a time-varying number of targets, we also propose a novel region-partitioning-based ES-TBD (RP-TBD) algorithm. By exploiting the common information shared between the batches of measurement data and the modeling merit-function-integrated particle filters (PFs), the RP-TBD partitions the observation region into a predicted subregion and an innovative subregion. The RP-TBD algorithm detects newborn targets in the innovative subregion, while maintains tracks of known targets in the predicted subregion. Experimental results using real ViSAR images show that the proposed algorithms outperform the state-of-the-art algorithms on detecting and tracking multiple dim targets in terms of location accuracy and false-alarm suppression.

中文翻译：

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ViSAR影像中运动目标阴影的同时检测和跟踪

视频合成孔径雷达 (ViSAR) 可以以高帧率获取感兴趣区域的高分辨率图像。ViSAR 的这一特性有助于实时检测和跟踪运动目标。使用 ViSAR 图像进行运动目标跟踪是一个典型的弱目标跟踪问题。在本文的上下文中，昏暗的目标对应于投射到静止背景场景上的移动车辆的阴影，与背景杂波相比，这些阴影出现在较低的灰度级。为了检测和跟踪 ViSAR 图像中的多个缓慢机动目标，我们提出了一种新算法，即基于扩展和收缩策略的粒子滤波器/基于动态规划的跟踪前检测 (ES-TBD) 算法。据我们所知，我们的工作代表了第一个使用 TBD 方法处理 ViSAR 检测和跟踪问题的算法。此外，为了检测和跟踪随时间变化的目标数量，我们还提出了一种新的基于区域划分的 ES-TBD (RP-TBD) 算法。通过利用批量测量数据和建模评价函数集成粒子滤波器 (PF) 之间共享的公共信息，RP-TBD 将观察区域划分为预测子区域和创新子区域。RP-TBD 算法检测创新子区域中的新生目标，同时保持预测子区域中已知目标的轨迹。