This paper presents a novel Gaussian mixture multi-model belief propagation (GMM-BP) filter for maneuvering multitarget tracking with multiple sensors. The filter is built upon the BP-based multisensor-multitarget tracking scheme, enabling accurate estimation of target numbers and states. It assumes linear Gaussian target motion, birth process, and sensor measurement models and utilizes the Gaussian mixture model to represent the target's marginal posterior probability density function. Additionally, multiple models are incorporated for target motion, enhancing the maneuvering target tracking capability. Closed-form recursions for means, covariances, and weights of Gaussian components in the filter are derived. To enhance scalability with the number of sensors, a simplified multi-sensor data fusion process is proposed, thereby preserving a linear relationship between Gaussian components and the number of sensors. The performance of the filter was evaluated in a three-dimensional (3D) simulated environment. Compared to the particle-implemented BP filter and the labeled multi-Bernoulli (LMB) filter, the proposed GMM-BP filter exhibited a significant improvement in computational efficiency while maintaining a high level of tracking accuracy.

中文翻译：

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用于多传感器多目标跟踪的高斯混合多模型置信传播滤波器

本文提出了一种新颖的高斯混合多模型置信传播（GMM-BP）滤波器，用于使用多个传感器进行多目标跟踪。该滤波器建立在基于BP的多传感器多目标跟踪方案的基础上，能够准确估计目标数量和状态。它假设线性高斯目标运动、出生过程和传感器测量模型，并利用高斯混合模型来表示目标的边际后验概率密度函数。此外，还结合了目标运动的多个模型，增强了机动目标的跟踪能力。导出滤波器中高斯分量的均值、协方差和权重的闭合形式递归。为了增强传感器数量的可扩展性，提出了一种简化的多传感器数据融合过程，从而保持高斯分量和传感器数量之间的线性关系。滤波器的性能在三维 (3D) 模拟环境中进行评估。与粒子实现的 BP 滤波器和标记的多伯努利 (LMB) 滤波器相比，所提出的 GMM-BP 滤波器在保持高水平的跟踪精度的同时，在计算效率方面表现出显着的提高。