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
In this paper, we account for radio-location experiments aiming at both indoor navigation and mobility detection applications for Wireless Body Area Networks (WBAN). This measurement campaign involved IEEE 802.15.4-compliant integrated radio devices organized within a full mesh topology over on-body and off-body links. The latter devices produce peerto-peer Received Signal Strength Indicators (RSSI) that could feed ranging, positioning or tracking algorithms. An in-depth behavioral analysis of the collected time-stamped radio-location metrics is thus proposed with respect to the captured human mobility (including body shadowing). Based on our observations and interpretations, practical insights are finally drawn in terms of system and algorithms design.
International audienceIn this paper we present the results of real-life localization experiments performed in an unprecedented cooperative and heterogeneous wireless context. These measurements are based on ZigBee and orthogonal frequency division multiplexing (OFDM) devices, respectively endowed with received signal strength indicator (RSSI) and round trip delay (RTD) estimation capabilities. More particularly we emulate a multi-standard terminal, moving in a typical indoor environment, while communicating with fixed OFDM-based femto-base stations (Femto-BSs) and with other mobiles or fixed anchor nodes (through peer-to-peer links) forming a wireless sensor network (WSN). We introduce the measurement functionalities and metrics, the scenario and set-up, providing realistic connectivity and obstruction conditions. Out of the experimental data, preliminary positioning results based on cooperative and geometric algorithms are finally discussed, showing benefits through mobile-to-mobile cooperation, selective hybrid data fusion and detection of unreliable nodes
International audienceIn this paper, we propose a geometric positioning technique for cooperative localization in Wireless Networks. The proposed technique is based on building geometric constraints from radio observables. These constraints are then merged to estimate the true position. In this paper we address situations where blind nodes, due to the lack of radio observables, cooperate to estimate their positions. Compared to other techniques, Monte Carlo simulations illustrate interesting performances in terms of positioning accuracy and computation complexity and runtime. © 2017 IEEE
Abstract-We consider positioning in the scenario where only two reliable range estimates, and few less reliable power observations are available. Such situations are difficult to handle with numerical maximum likelihood methods which require a very accurate initialization to avoid being stuck into local maxima. We propose to first estimate the support region of the two peaks of the likelihood function using a set membership method, and then decide between the two regions using a rule based on the less reliable observations. Monte Carlo simulations show that the performance of the proposed method in terms of outlier rate and root mean squared error approaches that of maximum likelihood when only few additional power observations are available.
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