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Maintaining Track Continuity for Extended Targets Using Gaussian-Mixture Probability Hypothesis Density Filter

机译:使用高斯混合概率假设密度滤波器维持扩展目标的轨道连续性

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摘要

A multiextended-target tracker based on the extended target Gaussian-mixture probability hypothesis density (ET-GMPHD) filter, which can provide the tracks of the extended targets, is proposed to maintain the track continuity for the extended targets. To identify the extended targets, each individual Gaussian term of the mixture representing the posterior intensity function will be assigned a label, which is evolved through time. Then a track management scheme, including track initiation, track confirmation, track propagation, and termination, is developed to form the tracks for the extended targets. Furthermore, to improve the performance of the extended target tracker we also propose a mixture partitioning algorithm for resolving the identities of the extended targets in close proximity. The simulation results show that our proposed tracker achieves the less error of the position estimates and decreases the probability of incorrect label assignments from 0.6 to 0.25.
机译:提出了一种基于扩展目标高斯 - 混合概率假设密度(ET-Gmphd)滤波器的多展 - 目标跟踪器,其可以提供扩展目标的轨道,以维持扩展目标的轨道连续性。为了识别扩展目标,将分配代表后部强度函数的混合物的每个单独的高斯术语将被分配一个标签,这通过时间演变。然后,开发了一种轨道管理方案,包括跟踪启动,跟踪确认,跟踪传播和终止,以形成扩展目标的曲目。此外,为了提高扩展目标跟踪器的性能,我们还提出了一种混合分区算法,用于解决密切接近的扩展目标的标识。仿真结果表明,我们所提出的跟踪器实现了估计误差越少,并降低了0.6到0.25的错误标签分配的概率。

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