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PHD filters of higher order in target number

机译：目标编号中较高阶的PHD过滤器

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摘要

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In this paper we show that this is indeed possible. We derive a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number.
机译：多目标递归贝叶斯非线性滤波器是理论上用于多传感器多目标检测，跟踪和识别的最佳方法。对于适合使用此过滤器的应用程序，仅对少量目标而言，它可能很容易处理。在较早的论文中，我们基于称为概率假设密度（PHD）的一阶多目标矩的传播，得出了该滤波器的近似形式的闭式方程。在最近的一篇论文中，Erdinc，Willett和Bar-Shalom提出了对PHD型滤波器的需求，该滤波器在单个目标的状态下保持一阶，​​但在目标数量上处于高阶。在本文中，我们证明了这确实是可能的。我们推导了一个封闭形式的基数化PHD（CPHD）过滤器，该过滤器不仅传播PHD，而且传播目标数上的整个概率分布。

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