多目标跟踪与数据关联问题的概率生成函数目录
A Catalog of Probability Generating Functionals for Multitarget Tracking and Data Assignment Problems
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- Metron（梅特隆公司）
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中文总结 AI 辅助
本文提出多目标跟踪与数据关联问题的概率生成函数目录，涵盖带标签、无标签及混合目标滤波器，推导区间滤波概率生成泛函与关联概率计算新方法，获粒子权重低复杂度近似。
中文摘要 AI 辅助
本文研究一类贝叶斯跟踪滤波器，其联合目标-测量过程的概率生成泛函可从定义该问题的统计假设中推导得出。该类滤波器涵盖带标签目标、无标签目标的滤波器，以及同时存在带标签与无标签目标的混合滤波器。新成果包括带标签和无标签目标轨迹区间滤波的概率生成泛函，以及一种计算测量-目标关联概率的概率生成函数的新方法。概率生成泛函为粒子滤波器实现中的重要权重计算提供精确表达式，通过鞍点法可从概率生成泛函推导出粒子权重的低计算复杂度近似。
英文摘要
This paper studies the class of Bayesian tracking filters for which the probability generating functional of the joint target-measurement process can be derived from the statistical assumptions that define the problem. The class includes filters for labeled and unlabeled targets, as well as hybrid filters in which both labeled and unlabeled targets are present. New results include the probability generating functional for interval filtering of target trajectories for both labeled an unlabeled targets, and a novel method for computing the probability generating function of measurement-to-target assignment probabilities. Probability generating functionals give exact expressions for calculating the importance weights in particle filter implementations. Low computational complexity approximations to the particle weights are derived from the probability generating functionals via the saddle point method.
