PyLayers: An open source dynamic simulator for indoor propagation and localization

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

Evaluation of a geometric positioning algorithm for hybrid wireless networks

International audienceIn this paper, we propose a geometric positioning method for hybrid wireless networks, based on a set membership method. Three common types of radio observables are considered for the position estimation: range, difference of ranges and received power. This paper details how to build geometric constraints from observables, and how to merge them to estimate the position. Given a realistic scenario, Monte Carlo simulation shows that the performance of the proposed method in terms of root mean squared error and cumulative density functions outperforms that of a numerically optimized maximum likelihood

Hybrid Data Fusion Techniques for Localization in UWB Networks

International audienceIn this paper, we exploit the concept of data fusion in UWB (Ultra Wide Band) localization systems by using different location-dependent observables. We combine ToA (Time of Arrival) and RSS (Received Signal Strength) in order to get accurate positioning algorithms.We assume that RSS observables are usually available and we study the effect of adding ToA observables on the positioning accuracy. The proposed architecture of Hybrid Data Fusion (HDF) is based on two stages: Ranging using RSS and ToA; and Estimation of position by the fusion of estimated ranges. In the first stage, we propose a new estimator of ranges from RSS observables assuming a path loss model. In the second stage, a new ML estimator is developed to merge different ranges with different variances. In order to evaluate these algorithms, simulations are carried out in a generic indoor environment and Cramer Rao Lower Bounds (CRLB) are investigated. Those algorithms show enhanced positioning results at reasonable noise levels

Enhancing positioning accuracy through direct position estimators based on hybrid RSS data fusion

International audienceIn 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

Enhancing Positioning Accuracy Through RSS Based Ranging And Weighted Least Square Approximation

International audienceIn this paper, localization based on Received Signal Strength (RSS) is investigated assuming a path loss log normal shadowing model. RSS-based estimation schemes of ranges are investigated; three different schemes are studied: Mean, median and mode. Estimation of position is performed using weighted least square approximation. We show that the positioning accuracy depends on the used estimator of ranges from RSS observables. We suggest that typical median estimator must be replaced by maximum likelihood estimator (mode) to enhance the positioning accuracy. Monte Carlo simulations show that the estimation scheme based on the mode estimator performs better than those based on the median or the mean estimator; and that the use of Weighted Least square approximation enhances the accuracy comparing to typical unweighted least square approximation

Improved Mobility Modeling for Indoor Localization Applications

International audienceThis paper presents a novel mobility model to perform realistic simulations of human movements and behaviors. The proposed model is based on discrete event simulation and graph theory. The proposed model is implemented in a wireless propagation simulator and used to evaluate various wireless network protocols including: propagation, localization and communication

A Maximum Likelihood TOA Based Estimator For Localization in Heterogeneous Networks

International audienceIn this paper, we exploit the concept of data fusion in hybrid localization systems by combining different TOA (Time of Arrival) observables coming from different RATs (Radio Access Technology) and characterized by different precisions in order to enhance the positioning accuracy. A new Maximum Likelihood estimator is developed to fuse different measured ranges with different variances. In order to evaluate this estimator, Monte Carlo simulations are carried out in a generic environment and Cramer Rao Lower Bounds (CRLB) are investigated. This algorithm shows enhanced positioning accuracy at reasonable noise levels comparing to the typical Weighted Least Square estimator. The CRLB reveals that the choice of the number, and the configuration of Anchor nodes, and the type of RAT may enhance positioning accuracy

MIMO UWB Systems based on Linear Precoded OFDM for Home Gigabit Applications

International audienceIn this paper, we investigate the use of multiple-input multiple-output (MIMO) techniques with linear precoded orthogonal frequency division multiplexing (LP-OFDM) waveform for high data rate ultra-wideband (UWB) systems. This scheme is an evolution of the multiband OFDM (MB-OFDM) solution supported by the WiMedia Alliance. The aim of this paper is to obtain a very high data rate of around one gigabit for home access networks (HAN) and to improve the system range for lower data rates, while not significantly increasing the system complexity compared to the WiMedia solution. Firstly, a single-input single-output (SISO) LP-OFDM system study is led to high-light the benefits of adding a precoding function to an OFDM signal in the UWB context. In an analytical study, different sys-tem choices and parameterization strategies are proposed in or-der to minimize the mean bit-error-rate (BER) and consequently improve the system range. Secondly, a MIMO scheme is added and global system simulations are performed on a proposed new geometric statistic MIMO channel model. We show that the pro-posed system can considerably improve the system range at low data rates, and can reach very high data rates up to 1 Gbit/s with comparable BER performances to WiMedia

Impact of on-body channel models on positioning success rate with UWB Wireless Body Area Networks

International audienceIn this paper, we aim to evaluate the positioning success rate of nodes placed on the body using different scheduling strategies at the Media Access Control (MAC) layer with Ultra Wide Band (UWB) Wireless Body Area Networks (WBAN) and under three different channel models. For this purpose, each node calculates its relative position with the estimation of its distances with the on-body anchors. Accordingly, the distance between two nodes can be estimated with the transmission of three packets, as defined by the '3-Way ranging' protocol (3-WR). However, these transactions can be affected by the WBAN channel leading into a packet loss and therefore positioning errors. In this work, we consider a PHY layer based on Impulse-Radio UWB (IR-UWB) with three different channels: (a) a theoretical path loss channel model based on the on-body CM3 channel (Anechoic chamber), (b) a simulated channel calculated with the PyLayers ray-tracing simulator and (c) experimental traces obtained by measurement. Moreover, we analyze the positioning success rate using three scheduling strategies (Single node localization (P2P), Broadcast Single node localiza-tion (P2P-B) and Aggregated & Broadcast (A&B)) with a MAC layer based on time division multiple access (TDMA) and under a realistic pedestrian walking scenario. Our results show that the scheduling strategy with A&B let the nodes to estimate more positions even through channels with slow and fast fading

Evaluation of a geometric positioning algorithm for hybrid wireless networks

International audienceIn this paper, we propose a geometric positioning method for hybrid wireless networks, based on a set membership method. Three common types of radio observables are considered for the position estimation: range, difference of ranges and received power. This paper details how to build geometric constraints from observables, and how to merge them to estimate the position. Given a realistic scenario, Monte Carlo simulation shows that the performance of the proposed method in terms of root mean squared error and cumulative density functions outperforms that of a numerically optimized maximum likelihood