Robust indoor localization and tracking using GSM fingerprints

Abstract

The article presents an easy to implement approach for indoor localization and navigation that combines Bayesian filtering with support vector machine classifiers to associate high-dimensionality cellular telephone network received signal strength fingerprints to distinct spatial regions. The technique employs a "space sampling" and a "time sampling" scheme in the training procedure, and the Bayesian filter allows introducing a priori information on room layout and target trajectories, resulting in robust room-level indoor localization.

Machine Learning for In-Region Location Verification in Wireless Networks

“…In [30], it is shown that SVM and LS-SVM are equivalent under mild conditions. From constraints (19) and the fact that t i = ±1 we have…”

“…The last equality comes from the strong law of large numbers. In the limit, the optimization problem (19) is equivalent to…”

“…where we dropped constraints (19) by using (20). The optimization problem is the same as of NN design and from [26], with the pair (w * , b * ) minimizing (22) and parametrizing (18), we havẽ t(a (i) ) ≈ P(H 0 |a (i) ) − P(H 1 |a (i) ).…”

“…For example, in [18], the single location verification problem is solved without assumptions on the channel model by applying logistic regression. In [19], the objective is to determine the position of a user inside a building by means of a multi-class support vector machine (SVM). Nevertheless, neither [18] nor [19] compare the performance of their ML approaches with that of the N-P test.…”

“…In [19], the objective is to determine the position of a user inside a building by means of a multi-class support vector machine (SVM). Nevertheless, neither [18] nor [19] compare the performance of their ML approaches with that of the N-P test.…”