Location awareness is a key factor for a wealth of wireless indoorapplications. Its provision requires the careful fusion of diverse informationsources. For agents that use radio signals for localization, this informationmay either come from signal transmissions with respect to fixed anchors, fromcooperative transmissions in between agents, or from radar-like monostatictransmissions. Using a-priori knowledge of a floor plan of the environment,specular multipath components can be exploited, based on a geometric-stochasticchannel model. In this paper, a unified framework is presented for thequantification of this type of position-related information, using the conceptof equivalent Fisher information. We derive analytical results for theCram\'er-Rao lower bound of multipath-assisted positioning, consideringbistatic transmissions between agents and fixed anchors, monostatictransmissions from agents, cooperative measurements in-between agents, andcombinations thereof, including the effect of clock offsets and missingsynchronization. Awareness of this information enables highly accurate androbust indoor positioning. Computational results show the applicability of theframework for the characterization of the localization capabilities of someenvironment, quantifying the influence of different system setups, signalparameters, and the impact of path overlap.
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