In this article, we propose a multisensor cardinalized probability density hypothesis (CPHD) filter for tracking an unknown number of targets that may maneuver over time by using a sensor network with partially overlapping fields of views (PO-FoVs). We develop a novel, Gaussian mixture particle (GMP) implementation of the jump-Markov CPHD filter to deal with highly non-linear/non-Gaussian models and target maneuvers. The concepts of zero-forcing and zero-avoiding originally used in density approximation are introduced to elucidate a key difference between geometric and arithmetic averaging approaches, which are extended for joint target-state and mode fusion with regard to each PO-FoV for which distributed flooding is used for internode communication. The resulting GMP-JMCPHD fusion algorithm comprises three FoV-oriented steps: splitting, fusion, and merging. Simulations are provided to demonstrate the effectiveness of the proposed approaches.

中文翻译：

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高斯混合粒子跳跃-Markov-CPHD融合技术，用于有限视角传感器的多目标跟踪

在本文中，我们提出了一种多传感器基数化概率密度假设（CPHD）过滤器，该跟踪器通过使用具有部分重叠视场（PO-FoVs）的传感器网络来跟踪可能随着时间而回旋的未知数量的目标。我们开发了一种新的，高斯混合马尔可夫CPHD滤波器的高斯混合粒子（GMP）实现，以处理高度非线性/非高斯模型和目标机动。引入了最初在密度近似中使用的零强迫和避免零的概念，以阐明几何平均和算术平均方法之间的关键区别，这些方法针对针对每个分布式PO-FoV的联合目标状态和模式融合进行了扩展泛洪用于节点间通信。生成的GMP-JMCPHD融合算法包括三个面向FoV的步骤：拆分，融合，和合并。提供仿真以证明所提出方法的有效性。