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

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

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

A Hybrid Positioning Method Based on Hypothesis Testing

International audienceWe 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

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

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 analog-digital processing system for amplitude-monopulse RSSI-based MiMo wifi direction-of-arrival estimation

We present a cost-effective hybrid analog digital system to estimate the Direction of Arrival (DoA) of WiFi signals. The processing in the analog domain is based on simple wellknown RADAR amplitude monopulse antenna techniques. Then, using the RSSI (Received Signal Strength Indicator) delivered by commercial MiMo WiFi cards, the DoA is estimated using the socalled digital monopulse function. Due to the hybrid analog digital architecture, the digital processing is extremely simple, so that DoA estimation is performed without using IQ data from specific hardware. The simplicity and robustness of the proposed hybrid analog digital MiMo architecture is demonstrated for the ISM 2.45GHz WiFi band. Also, the limitations with respect to multipath effects are studied in detail. As a proof of concept, an array of two MiMo WiFi DoA monopulse readers are distributed to localize the two-dimensional position of WiFi devices. This costeffective hybrid solution can be applied to all WiFi standards and other IoT narrowband radio protocols, such us Bluetooth Low Energy or Zigbee.This work was supported in part by the Spanish National Projects TEC2016-75934-C4-4-R, TEC2016-76465-C2-1-R and in part by Regional Seneca Project 19494/PI/14