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Generalized CPHD filter modeling spawning targets

机译：通用CPHD过滤器建模生成目标

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摘要

In some multi-target tracking applications, appearing targets are suitably modeled as spawning from existing targets. However, in the original cardinalized probability hypothesis density (CPHD) filter, this type of model is not included; instead appearing targets are modeled by spontaneous birth only. Recently, two versions of CPHD filter modeling spawning targets have already been developed, but these two methods are tractable only when the spawning targets cardinality distribution is restricted to be the Bernoulli distribution, the Poisson distribution or the Zero-inflated Poisson distribution. In this paper, we derive a generalized CPHD filter which is tractable and has no constraint of the cardinality distribution of the spawning targets, that is to say, the spawning targets cardinality distribution can be arbitrary. The derivation is based on the finite set statistics (FISST) and the Faa di bruno's determinant formula. Moreover, how this generalized CPHD filter degrades into the two previous versions is also given in this paper. The resulting filter is different from the original CPHD filter in two aspects: first, the prediction equation of the PHD function changes to be identical with that of the probability hypothesis density (PHD) filter; and second, the cardinality distribution prediction equation is now an expression including the cardinality distribution information of the spawning targets. Simulation results show that the proposed method can response much faster than the original CPHD filter in target number estimate when spawning targets appear, and has a much smaller cardinality estimate variance than the PHD filter and the original CPHD filter. A comparison considering the optimal sub-pattern assignment (OSPA) metric also demonstrates the good performance of the proposed method.
机译：在某些多目标跟踪应用程序中，将出现的目标适当地建模为从现有目标中产生的目标。但是，在原始的归一化概率假设密度（CPHD）过滤器中，不包括这种类型的模型。相反，出现的目标仅通过自然诞生来建模。最近，已经开发了两种版本的CPHD过滤器建模产卵目标，但是仅当产卵目标基数分布限制为伯努利分布，泊松分布或零膨胀泊松分布时，这两种方法才是易于处理的。在本文中，我们推导了一种通用的CPHD滤波器，该滤波器易于处理且不受产卵目标基数分布的约束，也就是说，产卵目标基数分布可以是任意的。该推导基于有限集统计量（FISST）和Faa di bruno的行列式公式。此外，本文还给出了该通用CPHD滤波器如何降级为先前的两个版本。所得到的滤波器在两个方面与原始CPHD滤波器不同：首先，PHD函数的预测方程式更改为与概率假设密度（PHD）滤波器的预测方程式相同。其次，基数分布预测方程式现在是一个包含产卵目标基数分布信息的表达式。仿真结果表明，该算法在出现产卵目标时，其目标数量估计的响应速度比原始CPHD过滤器快得多，并且基数估计方差比PHD过滤器和原始CPHD过滤器小。考虑最佳子模式分配（OSPA）指标的比较也证明了该方法的良好性能。

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  • 来源
    《Signal processing》 |2016年第11期|48-56|共9页
  • 作者

    Peiliang Jing; Jiangwei Zou; Yu Duan; Shiyou Xu; Zengping Chen;

  • 作者单位

    Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China;

    Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China;

    Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China;

    Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China;

    Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Cardinality distribution; Finite set statistics (FISST); Faa di bruno's determinant formula;

    机译：基数分布;有限集统计（FISST）;Faa di bruno的行列式;

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