Abstract :There has been growing interest in indoor positioning system technologies due to the important role of real-time indoor positioning services in modern technologies such as security services and emergency healthcare. Currently, many large companies such as Apple, Microsoft, and Google have researched location-based services (LBS), as they are key for network optimization and extensive computing applications. Multiple techniques were proposed using fingerprinting-based location methods due to their ability to obtain accurate results within several meters. However, their major drawback is that the received signal strength (RSS) can fluctuate with time and different environment, giving RSS distribution a multimodal distribution. Thus, in this paper, we established a framework consisting of k-mean-symmetrical-Hölder-divergence, a statistical model that encapsulates Cauchy-Schwarz divergence and skews Bhattacharyya divergence, to measure dissimilarities among signals that have multivariate distributions. In other words, the traditional k-mean is extended to meta-algorithms to detect the cluster that RSS is related to. Our second approach was hierarchical Bayesian model based on adaptive lasso criterion to recover sparse signals to optimize the accuracy of the indoor location estimation by solving the l1-minimization problem. The experimental results showed that the proposed system had substantially improved localization estimation accuracy compared to traditional fingerprinting-based localization methods.