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International audience, — In this paper, we introduce PyLayers a new open source radio simulator built to tackle indoor localization problem. PyLayers has been designed to simulate complete dynamic scenarios including the realistic movement of persons inside a building, the transmission channel estimation for multiple radio access technologies and the position estimation relying on location-dependent parameters originated from the simulated OSI physical layer. The channel is estimated by using a fast graph-based ray tracing method. From these simulated data, location dependent parameters, such as received power or time of arrival, can be deduced. The realistic movement of persons into the building layout is modeled with a virtual forces approach. The simulated data can be directly used with one of the built-in localization algorithms or be exported to various standards extensions. Finally, the accuracies of both the channel estimation and the localization are compared to measurements and show a good match.

International audience, In this paper, localization based on Received Signal Strength (RSS) is investigated assuming a path loss log normal shadowing model. On the one hand, indirect RSS-based estimation schemes are investigated; these schemes are based on two steps of estimation: estimation of ranges from RSS and then estimation of position using weighted least square approximation. We show that the performances of this type of schemes depend on the used estimator in the first step.We suggest that typical median estimator must be replaced by maximum likelihood estimator (mode) to enhance the positioning accuracy. On the other hand, a new direct RSS-based estimation scheme of position is proposed; Monte Carlo simulations show that the new estimator performs better than indirect estimators and can be reliable in future hybrid localization systems.