Localization with heterogeneous information

Although during the last decade considerable efforts have been invested in the integration of different wireless technologies, a new surge of interest is arising due to the upcoming internet of things (IoT) in which many relevant application scenarios rely on location information. However, due to the heterogeneity of the devices, ergo the heterogeneity of information available, novel indoor positioning algorithms capable to account for different types of information must be designed. Differently from the vast majority of localization solutions currently available which rely on one specific type of observation, e.g. range information only, in this article we consider the localization problem of multiple sources from range and angle measurements. To this end we first study the benefit of heterogeneous information via the rigidity theory and the Cramèr-Rao Lower Bound (CRLB) and then we show how to utilize an extension of the Euclidean-kernel, i.e. the Edge-kernel, to perform robust positioning under Non-Line-of-Sight (NLOS) conditions. In particular with reference to the latter contribution it is shown how to exploit the robust principal component analysis theory to improve the edge-kernel recovery and in turn the estimated target´s locations.