Preliminary Experiments on the Use of Nonlinear Programming for Indoor Localization

The research on methods to provide accurate and robust estimates of the positions of mobile devices in indoor scenarios is motivated by the urge to provide users with advanced location-based services. This paper presents preliminary experiments on an algorithm that enables software agents hosted on mobile devices to obtain estimates of their positions in known indoor environments if appropriate beacons are available at known locations. The experimented algorithm assumes that agents can use the sensors that devices provide to actively estimate their positions inside the environment by measuring the distances from the available beacons. In order to reduce the effects of the errors caused by the positions of the beacons, the discussed algorithm relies on the localization as optimization approach. In particular, the algorithm uses nonlinear programming to effectively solve the optimization problem that originates from the localization problem. Experimental results show that the discussed algorithm guarantees an improved level of robustness and accuracy with respect to a well-known and established alternative.