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A Track Quality Based Metric for Evaluating Performance of Multitarget Filters

机译：基于跟踪质量的指标，用于评估多目标过滤器的性能

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摘要

The track qualities of multitarget state estimates are often available in many tracking algorithms, including the multiple hypothesis tracking and the joint integrated probabilistic data association estimators. However, the recently proposed Optimal Subpattern Assignment (OSPA) metric ignores the quality information and thus its capability to quantify the performance of multi-object estimation algorithms is limited. In this paper, a new metric, called the quality-based OSPA (Q-OSPA), is proposed based on the original OSPA metric. The proposed Q-OSPA metric is able to incorporate the quality information and thus provide more accurate quantification of the performance of multi-object estimation algorithms. Also, the mathematical consistency of the original OSPA metric is maintained by the proposed Q-OSPA metric. In addition, if the qualities of estimates are not available, the Q-OSPA metric reduces to the original OSPA metric by assigning equal qualities to the estimates. Besides theoretical derivations, simulations are presented to verify the advantages of the proposed metric.
机译：多目标状态估计的跟踪质量通常可在许多跟踪算法中使用，包括多重假设跟踪和联合集成概率数据关联估计器。但是，最近提出的最佳子模式分配（OSPA）度量标准忽略了质量信息，因此其量化多对象估计算法性能的能力受到限制。在本文中，基于原始OSPA指标，提出了一种新的指标，称为基于质量的OSPA（Q-OSPA）。所提出的Q-OSPA度量能够合并质量信息，从而为多目标估计算法的性能提供更准确的量化。而且，原始OSPA指标的数学一致性由建议的Q-OSPA指标保持。另外，如果估计质量不可用，则Q-OSPA度量通过为估计分配相同的质量而降低到原始OSPA度量。除了理论推导外，还提供了仿真来验证所提出度量的优势。

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