论文标题
家庭监控平台内的室内定位技术
Indoor Localization Techniques Within a Home Monitoring Platform
论文作者
论文摘要
本文详细介绍了许多用于实时监测老年人的室内定位技术。这些是在由欧盟资助的I-Light Research项目框架内开发的。该项目针对可配置和成本效益的网络物理系统的开发和初步评估，以监视生活在自己家中的老年人的安全。本地化硬件由许多替换现有灯具的自定义开发设备组成。除了照明功能外，它们还测量了用户向用户发出的蓝牙低能信号的强度。读数是实时记录的，并发送到软件服务器进行分析。我们介绍了几种服务器端算法所达到的准确性的比较评估，包括Kalman Filtering，Kalman Filtering，外观的启发式以及基于神经网络的方法。众所周知，基于测量信号强度的方法对墙壁的放置，所使用的建筑材料，门的存在以及现有家具敏感。因此，我们评估了具有不同建筑特征的两个独立位置中提出的方法。我们表明，所提出的技术提高了本地化的准确性。作为最后一步，我们根据现有方法评估结果。
This paper details a number of indoor localization techniques developed for real-time monitoring of older adults. These were developed within the framework of the i-Light research project that was funded by the European Union. The project targeted the development and initial evaluation of a configurable and cost-effective cyber-physical system for monitoring the safety of older adults who are living in their own homes. Localization hardware consists of a number of custom-developed devices that replace existing luminaires. In addition to lighting capabilities, they measure the strength of a Bluetooth Low Energy signal emitted by a wearable device on the user. Readings are recorded in real time and sent to a software server for analysis. We present a comparative evaluation of the accuracy achieved by several server-side algorithms, including Kalman filtering, a look-back heuristic as well as a neural network-based approach. It is known that approaches based on measuring signal strength are sensitive to the placement of walls, construction materials used, the presence of doors as well as existing furniture. As such, we evaluate the proposed approaches in two separate locations having distinct building characteristics. We show that the proposed techniques improve the accuracy of localization. As the final step, we evaluate our results against comparable existing approaches.
