基于隐马尔可夫模型的智能手机房间识别
Room Recognition Using Discriminative Ensemble Learning with Hidden Markov Models for Smartphones
摘要：
精确的房间定位系统是提供基于位置的服务的强大工具。考虑到人们大部分时间都在室内度过，室内定位系统在设计智能环境中变得越来越重要。在这项工作中，我们提出了一种有效的集成学习方法，以提供智能建筑中的房间级定位。我们提出的定位方法实现了高的房间级定位精度相结合的隐马尔可夫模型与简单的判别式学习方法。定位算法是为基于终端的系统设计的，该系统由商用智能手机和Wi-Fi接入点组成。我们进行实验研究，以评估我们的系统在办公室一样的室内环境。实验结果表明，该系统可以克服传统的个体机器学习和集成学习方法。
英文摘要：
An accurate room localization system is a powerful tool for providing location-based services. Considering that people spend most of their time indoors, indoor localization systems are becoming increasingly important in designing smart environments. In this work, we propose an efficient ensemble learning method to provide room level localization in smart buildings. Our proposed localization method achieves high room-level localization accuracy by combining Hidden Markov Models with simple discriminative learning methods. The localization algorithms are designed for a terminal-based system, which consists of commercial smartphones and Wi-Fi access points. We conduct experimental studies to evaluate our system in an office-like indoor environment. Experiment results show that our system can overcome traditional individual machine learning and ensemble learning approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
