Unsupervised learning of indoor localization based on received signal strength

摘要

Most indoor wireless sensor network localization methods require costly site surveys to collect fingerprint information for later comparison. Moreover, due to the dynamic nature of fingerprint information in indoor wireless environments, the need for site surveys may be ongoing. In this work, indoor localization is addressed with an unsupervised learning algorithm. Our novel algorithm based on received signal strength combines the information conveyed by both range-based and range-free localization with state-of-art optimization techniques. A specially designed hierarchical Bayesian hidden Markov model coupled with a particle filter helps mitigate non-line-of-sight and multipath errors. This grid-based data sample process, derived from the theory of Dirichlet processes, simplifies the global optimization problem of unsupervised learning by employing a single initial hyper-parameter. Meanwhile, for obtaining accurate coordinates of mobile nodes, a unique semidefinite programming method is used to provide feedback to the radio propagation model. This feedback step can enable the grid-based algorithms not only to establish the coordinates of a mobile node, but also to optimize the accuracy iteratively. Theoretical and experimental analyses indicate that the proposed algorithm can achieve better localization accuracy than conventional range-based algorithms without adding computation cost. Copyright (c) 2016 John Wiley & Sons, Ltd.