Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

中文翻译：

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基于贝叶斯图模型的三维模式识别定位系统

无线局域网中的接入点部署在许多室内环境中。基于可用接收信号强度指标的无设备无线定位系统最近获得了相当多的关注，因为它们可以使用商用现成设备定位人员。大多数定位算法考虑导致定位精度低的二维模型。尽管可以使用 3 维定位模型，但由于它们使用了大量参考点，因此它们具有很高的计算和定位误差。在这项工作中，我们提出了一种基于贝叶斯图形模型的三维室内定位系统。所提出的模型已经通过基于指纹技术的实验进行了测试，该技术在离线训练阶段从每个接入点收集接收到的信号强度指标，然后在在线定位阶段估计用户位置。结果表明，所提出的模型使用比基准算法更少的参考点，实现了超过 25% 的高定位精度。