家庭监控平台中的室内定位技术
Indoor Localization Techniques Within a Home Monitoring Platform
摘要：
本文详细介绍了一些室内定位技术开发的实时监控老年人。这些是在欧洲联盟资助的i-Light研究项目框架内开发的。该项目的目标是开发和初步评估一个可配置和具有成本效益的网络物理系统，用于监测住在自己家中的老年人的安全。本地化硬件由许多定制开发的设备组成，可取代现有的灯具。除了照明功能外，它们还可以测量用户身上的可穿戴设备发出的蓝牙低功耗信号的强度。读数被实时的记录并发送到软件服务器进行分析。我们提出了几个服务器端算法，包括卡尔曼滤波，回顾启发式以及基于神经网络的方法实现的准确性的比较评估。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，我们在两个不同的位置具有不同的建筑特点，评估所提出的方法。我们表明，所提出的技术提高了定位的准确性。作为最后一步，我们评估我们的结果对可比的现有方法。
英文摘要：
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.
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