This paper deals with the problem of device’s location using Wi-Fi signal strength in an indoor environment. In order to improve the location accuracy a new fingerprinting-probabilistic algorithm is proposed. In this algorithm, the probability distribution of the Received Signal Strength (RSS) by a Mobile User (MU) from several Access Points (AP) is approximated using the Gaussian Mixture Model (GMM) approach. This probability distribution is then exploited to improve the location estimation of the mobile user. To tackle the initialization problem of the Expectation Maximization (EM) algorithm, which is used to estimate the Gaussian mixture parameters, a deterministic initialization method is proposed. This method named Manual initialization (MI) initializes manually the mixture parameters directly from the data. To cope with the MI shortcomings, an Adaptive Initialization (AI) technique is proposed. The performance of the resulting method, named the Improved GMM (IGMM) is evaluated experimentally and compared to that of other robust methods. The obtained results illustrate the efficiency of the IGMM method. Compared to the standard initialization technique, the MI and AI initialization techniques improve the location accuracy by 14.4% and 18.4%, respectively. Compared to other location methods, the improvement brought about by the IGMM method, varies from 5.1% to 17.7%, when the MI technique is used, and from 10% to 21.5%, when the AI technique is used.

本文使用Wi-Fi信号强度在室内环境中处理设备的位置问题。为了提高定位精度，提出了一种新的指纹概率算法。在此算法中，使用高斯混合模型（GMM）方法估算了移动用户（MU）从多个接入点（AP）接收信号强度（RSS）的概率分布。然后，利用该概率分布来改善移动用户的位置估计。为了解决期望值最大化算法用于估计高斯混合参数的初始化问题，提出了一种确定性的初始化方法。这种名为“手动初始化（MI）”的方法直接从数据中手动初始化混合参数。为了解决MI的缺点，提出了一种自适应初始化（AI）技术。实验评估了所得方法（称为改进GMM（IGMM））的性能，并将其与其他可靠方法的性能进行了比较。获得的结果说明了IGMM方法的效率。与标准初始化技术相比，MI和AI初始化技术分别将定位精度提高了14.4％和18.4％。与其他定位方法相比，IGMI方法带来的改进在使用MI技术时从5.1％到17.7％不等，在使用AI技术时从10％到21.5％不等。获得的结果说明了IGMM方法的效率。与标准初始化技术相比，MI和AI初始化技术分别将定位精度提高了14.4％和18.4％。与其他定位方法相比，IGMI方法带来的改进在使用MI技术时从5.1％到17.7％不等，在使用AI技术时从10％到21.5％不等。获得的结果说明了IGMM方法的效率。与标准初始化技术相比，MI和AI初始化技术分别将定位精度提高了14.4％和18.4％。与其他定位方法相比，IGMI方法带来的改进在使用MI技术时从5.1％到17.7％不等，在使用AI技术时从10％到21.5％不等。