论文标题
在室内定位数据集中加速加速本地化性能
Towards Accelerated Localization Performance Across Indoor Positioning Datasets
论文作者
论文摘要
室内场景中的本地化速度和准确性会极大地影响用户的体验质量。尽管许多单独的机器学习模型可以达到可比的定位性能，但它们的预测机制对系统提供了不同的复杂性。在这项工作中，我们提出了一种用于多构建和多层部署的指纹定位方法，该方法由三种用于建筑分类，地板分类和2D定位回归的级联组成。我们详尽地搜索级联的每个步骤中最佳性能，同时验证14个不同的公开可用数据集。结果，我们从整体定位准确性和处理速度方面提出了模型表现最佳的组合，并在独立的样本集上进行了评估。我们将平均预测时间减少了71％，同时在所有被考虑的数据集中实现了可比的定位性能。此外，在大量培训数据集的情况下，预测时间减少到基准的1％。
The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of three models for building classification, floor classification, and 2D localization regression. We conduct an exhaustive search for the optimally performing one in each step of the cascade while validating on 14 different openly available datasets. As a result, we bring forward the best-performing combination of models in terms of overall positioning accuracy and processing speed and evaluate on independent sets of samples. We reduce the mean prediction time by 71% while achieving comparable positioning performance across all considered datasets. Moreover, in case of voluminous training dataset, the prediction time is reduced down to 1% of the benchmark's.
