AROMA：定位系统无线电地图的自动生成
AROMA: Automatic Generation of Radio Maps for Localization Systems
摘要：
无线局域网定位是近年来无线局域网研究的热点。由于广泛的WLAN部署，WLAN定位提供无处不在的覆盖，并通过提供其用户的位置而无需使用任何额外的硬件来增加无线网络的价值。然而，WLAN定位系统通常需要构建无线电地图，这是WLAN定位系统部署的主要障碍。无线电地图存储关于来自感兴趣地点中的选定位置处的不同信号强度流的信号强度的信息。无线电地图的典型构建涉及测量和校准，使其成为繁琐且耗时的操作。在本文中，我们提出了AROMA系统，自动构建精确的主动和被动无线电地图的设备为基础的和无设备的WLAN定位系统。AROMA有三个主要目标：高精度、低计算要求和最小用户开销。为了实现高精度，AROMA使用3D光线追踪，并通过统一衍射理论（UTD）进行增强，以模拟电场行为和人体阴影效应。AROMA还可以自动执行一些常规任务，例如导入建筑模型和感兴趣区域的自动采样，以减少用户的开销。最后，AROMA使用许多优化技术来降低计算需求。我们提出了我们的系统架构，并描述了它的不同组件，使AROMA实现其目标的细节。我们在两个不同的测试平台上评估AROMA。我们的实验表明，预测的信号强度不同于测量的最大平均绝对误差为3.18 dBm，实现最大定位误差为2.44米的基于设备和无设备的情况下。
英文摘要：
WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.
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。
