多传感器GIW-PHD滤波器用于多扩展目标跟踪

Peng Li, Jinlong Yang, H. Ge, Huanqing Zhang
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引用次数: 3

摘要

高斯反Wishart概率假设密度滤波(GIW-PHD)是一种很有前途的多扩展目标形状估计跟踪算法。然而,据我所知,这种方法只能用于单传感器跟踪系统,对于复杂的跟踪场景,无法获得准确的状态估计。为了解决这一问题,我们采用多传感器注入技术提出了一种多传感器GIW-PHD方法,该方法适用于多传感器跟踪系统中对多个扩展目标的跟踪。首先,建立了三维场景下多传感器扩展目标的测量模型,并推导了状态更新的融合公式;仿真结果表明,该算法比传统的单传感器GIW-PHD具有更好的性能。
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Multi-sensor GIW-PHD filter for multiple extended target tracking
Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has proven to be a promising algorithm for multiple extended target tracking with shape estimation. However, as far as I know, this method only can be used in the single sensor tracking system, which cannot obtain the accurate state estimates for the complex tracking scenario. To solve this problem, we propose a multi-sensor GIW-PHD method by using the multiple sensor infusion technique, which is suitable to the multi-sensor tracking system for multiple extended target tracking. First, a novel measurement model of the extended target is constructed for multi-sensor in three-dimensional scenario, and then the fusion formulas of state update are derived. Simulation results show that the proposed algorithm has a better performance than that of the conventional GIW-PHD with a single sensor.
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