Level-Set Random Hypersurface Models for Tracking Nonconvex Extended Objects
Level-Set Random Hypersurface Models for Tracking Nonconvex Extended Objects
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DOI：
10.1109/taes.2016.130704
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发表时间：
2016-12-01
影响因子：
4.4
通讯作者：
Hanebeck, Uwe D.
中科院分区：
文献类型：
--
作者：
Zea, Antonio;Faion, Florian;Hanebeck, Uwe D.
本文提出了一种新的方法来跟踪一个非凸形状近似的扩展目标的基础上有噪声的点测量。为此，一种新的类型的随机超曲面模型（RHM）称为水平集RHM的介绍，模型的内部形状与水平集的隐函数。基于水平集RHM，可以推导出一个非线性测量方程，该方程允许采用标准高斯状态估计器来跟踪扩展对象，即使在具有中等测量噪声的情况下。在本文中，形状描述使用多边形，形状正则化应用活动轮廓模型的想法。
This paper presents a novel approach to track a nonconvex shape approximation of an extended target based on noisy point measurements. For this purpose, a novel type of random hypersurface model (RHM) called Level-set RHM is introduced that models the interior of a shape with level-sets of an implicit function. Based on the Level-set RHM, a nonlinear measurement equation can be derived that allows to employ a standard Gaussian state estimator for tracking an extended object even in scenarios with moderate measurement noise. In this paper, shapes are described using polygons, and shape regularization is applied using ideas from active contour models.