The evaluation of Indoor Positioning Systems (IPS) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.

中文翻译：

---

走向无处不在的室内定位：跨异构数据集比较系统

室内定位系统 (IPS) 的评估主要依赖于研究人员或合作伙伴设施中的本地部署。准备综合实验、收集数据和考虑多种场景的复杂性通常限制了评估领域，因此也限制了对拟议系统的评估。由于无法保证使用相同的传感器或锚点密度，因此无法概括受控实验的要求和特征。数据集的出现将 IPS 评估推到了与机器学习模型类似的水平，其中新提案在许多异构数据集上进行评估。本文提出了一种在多个场景中评估 IPS 的方法，该方法已通过三个用例进行验证。