Mohamed Laaraiedh

Enhancing Positioning Accuracy through Direct Position Estimators Based on Hybrid RSS Data Fusion

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.

Hybrid Data Fusion techniques for localization in UWB networks

In 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.

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

PyLayers is a new open source radio simulator. It has been designed to evaluate localization algorithm performances through the realistic simulation of location-dependent parameters (LDPs) in heterogeneous mobile radio networks. The radio channel is synthesized by using a novel graph-based ray tracing method which has been introduced in order to improve performances in mobile ray-tracing scenarios where geometrical information reuse from one mechanical time-step to another is advantageous. PyLayers can synthesized the narrow band, wide band or ultra wide band (UWB) channel impulse response and thus allows to produced various kind of location dependent parameters as the widely used LDPs received power an time of arrival. Realistic movement of pedestrian agents into the building layout is modeled with a virtual forces approach. The simulated data can be directly exploited with one of the original built-in localization algorithms or be exported to various standards file extensions for external post-processing. Examples of typical PyLayers outputs are provided.

Fingerprinting localization based on neural networks and ultra-wideband signals

Fingerprinting techniques have been proved as an effective techniques for determining the position of a mobile user in an indoor environment and in challenging environments such as mines, canyons, and tunnels where common localization techniques based on time of arrival (TOA) or received signal strength (RSS) are subject to big positioning errors. In this paper, a fingerprinting based localization technique using neural networks and ultra-wideband signals (UWB) is presented as an alternative. The fingerprinting database is built with signatures extracted from channel impulse responses (CIR) obtained by processing an IR-UWB indoor propagation measurement campaign. The construction of the neural networks and the adopted approach are described. Positioning performances are evaluated with different selected signatures and different sizes of the fingerprinting database.

Cooperative and heterogeneous indoor localization experiments

In 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.

Constrained LMDS technique for human motion and gesture estimation

Body Area Networks is an emerging domain taking a big interest from developers and system designers. On the other hand, the need to localize is becoming necessary in diverse applications. Within this context, the aim of this paper is to estimate the different gestures and motions of the human body. Initially, we use information, about human motion, extracted from C3D files. In fact, these files provide us with the exact 3D coordinates of the sensors on a moving body. In a second step the IEEE 802.15.6 channel model is used to estimate the distances between sensors which are the input of the locomotion technique based on Multidimensional Scaling. Basically, this technique did not present satisfying results, thats why we have improved our results by an SVD reconstruction algorithm and by adding distance constraints.

A Maximum Likelihood TOA Based Estimator For Localization in Heterogeneous Networks

In 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.

A Hybrid Positioning Method Based on Hypothesis Testing

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.

Ray tracing-based radio propagation modeling for indoor localization purposes

This paper regards the application of ray tracing (RT) tools for indoor localization techniques. The paper presents two possible applications of RT tools in that context. The first is the train/construction of fingerprints databases in order to complement or/and replace laborious measurement campaigns. The second application is the modeling of location-dependent parameters in order to feed localization techniques. These two applications are investigated and validated using three RT tools applied on a synthetic indoor environment. The obtained results showed that RT tools are reliable in both fingerprinting databases construction and LDPs modeling.

Overcoming singularities in TDoA Based location estimation using Total Least Square

In this paper, we propose an estimation scheme to overcome singularities which may deteriorate Time Difference of Arrival (TDoA) based localization accuracy when using Least Square (LS) estimator. The solution is based on Total Least Square (TLS) estimator. We provide an algorithm of localization which exploits the rank and the condition number of matrices in order to enhance accuracy. The Monte Carlo simulations show that the proposed algorithm outperforms the typical LS algorithm. It is also more robust to the erroneous TDoAs.