Álvarez-Merino Carlos S, Luo-Chen Hao Qiang, Khatib Emil Jatib, Barco Raquel
Instituto Universitario de Investigación en Telecomunicación (TELMA), University of Málaga, CEI Andalucia TECH E.T.S.I. Ingeniería de Telecommunication, Bulevar Louis Pasteur 35, 29010 Málaga, Spain.
Sensors (Basel). 2021 Oct 23;21(21):7020. doi: 10.3390/s21217020.
High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.
随着围绕5G出现的新型基于位置的服务，高精度室内定位正变得必不可少。由于参考点的高密度，高精度室内定位技术的部署通常成本高昂。在这项工作中，我们提出了几种不同技术的机会主义融合，例如超宽带（UWB）和WiFi精细时间测量（FTM），以提高定位性能。我们还提出了与蜂窝网络（如LTE）融合使用，以在参考点数量不足的情况下补充这些技术，提高定位服务的可用性。提出了最大似然估计（MLE）来加权不同的参考点以消除异常值，并针对定位算法提出并评估了几种搜索方法。使用UWB和WiFi FTM搭建了一个实验装置来验证所提出的系统，因为它们已被集成到最新的旗舰智能手机中。结果表明，在三边测量算法中使用多技术融合显著优化了精确覆盖区域。此外，通过对定位问题进行超定，它减少了定位误差。这种技术降低了任何面向定位服务的网络部署成本，因为每种技术所需的参考点数量减少了。
