With the increased resolution capability of modern sensors, an object should be considered as extended if the target extent is larger than the sensor resolution. Multiple maneuvering extended object tracking (MMEOT) uses not only measurements of the target centroid but also high-resolution sensor measurements which may resolve individual features or measurement sources. MMEOT aims to jointly estimate object number, centroid states, and extension states. However, unknown and time-varying maneuvers of multiple objects produce difficulties in terms of accurate estimation. For multiple maneuvering star-convex extended objects using random hypersurface models (RHMs) in particular, their complex maneuvering behaviors are difficult to be described accurately and handled effectively. To deal with these problems, this paper proposes an interacting multiple model Gaussian mixture probability hypothesis density (IMM-GMPHD) filter for multiple maneuvering extended object tracking. In this filter, linear maneuver models derived from RHMs are utilized to describe different turn maneuvers of star-convex extended objects accurately. Based on these, an IMM-GMPHD filtering recursive form is given by deriving new update and merging formulas of model probabilities for extended objects. Gaussian mixture components of different posterior intensities are also pruned and merged accurately. More importantly, the geometrical significance of object extension states is fully considered and exploited in this filter. This contributes to the accurate estimation of object extensions. Simulation results demonstrate the effectiveness of the proposed tracking approach—it can obtain the joint estimation of object number, kinematic states, and object extensions in complex maneuvering scenarios.

中文翻译：

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使用高分辨率传感器跟踪多个机动随机超曲面扩展对象

随着现代传感器分辨率能力的提高，如果目标范围大于传感器分辨率，则应将对象视为扩展对象。多机动扩展目标跟踪 (MMEOT) 不仅使用目标质心的测量值，还使用高分辨率传感器测量值，这些测量值可以解析单个特征或测量源。MMEOT 旨在联合估计对象数量、质心状态和扩展状态。然而，多个物体的未知和时变机动在准确估计方面产生困难。特别是对于使用随机超曲面模型（RHMs）的多机动星凸扩展物体，其复杂的机动行为难以准确描述和有效处理。为了应对这些问题，本文提出了一种交互多模型高斯混合概率假设密度（IMM-GMPHD）滤波器，用于多机动扩展目标跟踪。在该滤波器中，利用源自 RHM 的线性机动模型来准确描述星凸扩展物体的不同转弯机动。在此基础上，通过推导新的扩展对象模型概率更新和合并公式，给出了一个IMM-GMPHD过滤递归形式。不同后验强度的高斯混合分量也被精确地修剪和合并。更重要的是，该过滤器充分考虑和利用了对象扩展状态的几何意义。这有助于准确估计对象扩展。