DIRECT LOCALIZATION: AN ISING MODEL APPROACH

Accurate indoor localization is a challenging problem in a multipath environment. In order to tackle this problem, several methods have been proposed. Direct localization is one of these methods that makes use of a two-dimensional search in a planar geometry. In this paper, we use a compressed sensing framework in the direct localization technique to estimate the location of a user in an indoor multipath environment. We form a penalized $\ell_0$-norm structure for this problem, and then convert this structure to an Ising energy problem. The Ising energy problem is solved using Markov Chain Monte Carlo (MCMC). Our simulation results show that our approach improves the estimation accuracy compared to the existing methods in the literature.