Galván-Tejada Carlos E, García-Vázquez Juan Pablo, Brena Ramon F
Instituto Tecnologico de Monterrey, CETEC South Tower, 5th floor, Avenue. E. Garza Sada 2501, 64849, Monterrey NL, Mexico.
Sensors (Basel). 2014 Jun 20;14(6):11001-15. doi: 10.3390/s140611001.
User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user's location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.
用户室内定位一直在不断改进，特别是随着现代移动设备中集成了新的传感器，这使我们不仅能够利用诸如WiFi等日常使用的基础设施，还能利用自然基础设施，比如自然磁场。在本文中，我们基于对46个磁场信号特征的特征提取，展示了对当前室内定位模型的扩展和改进。该扩展在我们的方法中增加了一个特征选择阶段，这是通过遗传算法（GA）执行的，目的是优化当前模型的适应性。此外，我们在两种不同场景下对最终模型进行了评估：家庭和办公楼。结果表明，无论场景和房间位置分布如何，执行特征选择过程都能使我们将模型的信号特征数量从46个减少到5个。此外，我们验证了在两种场景下减少特征数量都会增加估计器正确检测用户位置的概率（灵敏度）及其检测误报的能力（特异性）。
