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基于机器学习的快速室内/室外过渡检测算法。

A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning.

机构信息

School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China.

Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China.

出版信息

Sensors (Basel). 2019 Feb 14;19(4):786. doi: 10.3390/s19040786.

DOI:10.3390/s19040786
PMID:30769914
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6412305/
Abstract

The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.

摘要

智能手机的广泛普及使得在各种复杂场景中提供基于位置的服务（LBS）成为可能。位置和上下文状态，特别是室内/室外切换，为无缝室内和室外定位和导航提供了直接指标。由于复杂场景中各种信号变化以及室内外信号源在 IO 转换区域的相似性，很难快速、高置信度地检测到室内外转换。在本文中，我们考虑在复杂场景中快速切换到 IO 转换区域并具有高检测精度的挑战。为此，我们从 Android 智能手机中的 GNSS 测量中分析并提取不同滑动窗口下的空间几何分布、时间序列和统计特征，并提出了一种新颖的基于堆叠和通过隐马尔可夫模型过滤检测结果的集成模型的 IO 检测方法。我们在四个数据集上评估了我们的算法。结果表明，我们提出的算法能够在我们收集数据的室内外环境中以 99.11%的准确率识别 IO 状态，在新的室内外场景中以 97.02%的准确率识别。此外，在我们收集数据的室内外切换场景中，识别准确率达到 94.53%，3 秒内切换延迟的概率超过 80%。在新场景中，识别准确率达到 92.80%，4 秒内切换延迟的概率超过 80%。

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