A. Bahillo

Adaptive Data Fusion for Wireless Localization in Harsh Environments

The dynamic and unpredictable characteristics of wireless channels in harsh environments have resulted in a poor performance of localization systems. Conventional implementations rely on unrealistic assumptions driven by tractability requirements, such as linear models or Gaussian errors. In this paper, we present a framework for data fusion in localization systems based on determining likelihood functions that represent the relationship between measurements and distances. In this framework, such likelihoods are dynamically adapted to the propagation conditions. The subsequent usage of a particle filter (PF) leads to an adaptive likelihood particle (ALPA) filter that addresses the nonlinear and non-Gaussian behavior of measurements over time. The ALPA filters performance is quantified by using received-signal-strength (RSS) and time-of-arrival (TOA) measurements collected with wireless local area network (WLAN) devices. We compare the accuracy obtained to the accuracy of conventional implementations and to the posterior Cramér-Rao lower bound (PCRLB). Both empirical and simulation results show that the proposed ALPA filter significantly improves the accuracy of conventional approaches, obtaining an error close to the PCRLB.

NLOS mitigation based on range estimation error characterization in an RTT-based IEEE 802.11, indoor location system

Ranging techniques have significant effects on localization accuracy, system complexity, and system cost, in most common wireless location systems. In such systems, loss of accuracy is most often caused by the lack of a direct line-of-sight (LOS) between a mobile user (MU) and a reference access point (AP). This drawback is increased when the location system is deployed in cluttered scenarios. Modeling the non-line-of-sight (NLOS) error formally represents one way to deal with this issue. However, a trade-off decision has to be made between the efficiency of the system and its complexity and computational cost. In this paper, NLOS error is confined to single-parameter distributions, specifically Exponential and Rayleigh. Both models are introduced in the prior NLOS measurements correction (PNMC) method, and compared in terms of range and position error, in a round-trip time (RTT)-based real location system. Furthermore, improvement of accuracy by dynamically estimating Exponential or Rayleigh parameters is likewise analyzed. The results show a high NLOS bias reduction after PNMC procedure, and even greater when using dynamic parameters.

Dynamic estimation of optimum path loss model in a RSS positioning system

The development of location techniques in urban and indoor environments by using measurements of received signal strength (RSS) has as a main drawback the fact that in such environments the value of RSS depends on many unpredictable and changeable factors, not only on the actual distance between mobile station (MS) and the different base stations (BSs). With the aim of solving these limitations of RSS-based location systems, in this paper we present a novel technique to find out which model best describes the different propagations of signals traveling between the MS and the different BSs. This method estimates accurately and dynamically, all the path loss exponents that model the signal attenuation in propagation paths to each BS, only by using the received signal strength measurements that are obtained in each moment. Therefore, our method does not need any previous knowledge about user position or kind of propagation environment. From the estimation of the different path loss exponents, we obtain much more accurate distance estimations than those obtained by using a generic and unique propagation model previously fixed. We present the results obtained by using the methods described in this paper, from real measurements taken in a GSM network as well as from computer simulations. By using the techniques presented in this paper we can see the great improvement in the precision on estimating distances from the MS and the different BSs. The results obtained from the real measurements support the simulations results and show how that improvement in the distance estimation makes the location much more accurate.

E-field assessment errors associated with RF dosemeters for different angles of arrival

In this paper, an analysis of the surface electric field on a human body based on finite-difference time-domain simulations is presented. A statistical analysis of the dosemeter interaction with the human body has been made by means of the variations of the relative orientation of the human body from the RF source. Variations of the RF source frequency have also been made, by comparing three different services FM, GSM-900 and DCS-1800. Three different scenarios have been simulated where the angle of arrival of the main RF contribution impinges on the human body with a certain probability. Despite the differences between the scenarios, the variations in the electric field strength at each frequency are negligible where the dosemeter would be located.

Pedestrian navigation in harsh environments using wireless and inertial measurements

The popularity of positioning satellite systems in open areas has led to a high demand for systems suitable for harsh environments, where the former fail. Wireless localization and inertial navigation have emerged as the most valuable alternatives to offer positioning in such environments. However, the characteristics of the wireless propagation channel in harsh environments are dynamic and unpredictable while the errors in inertial navigation rapidly increase with time. In this paper, we present a general framework and algorithms for data fusion in navigation systems. The presented techniques combine both wireless and inertial measurements. To assess the proposed methods, we collected measurements from commercial wireless devices and low-cost inertial sensors in a real indoor environment. The experimental results show the remarkable performance of the proposed method, capable of reaching a sub-meter accuracy.

Hybrid RSS-RTT localization scheme for wireless networks

The hybrid localization techniques have attracted significant research interest since a variety of localization metrics can be easily obtained by most wireless devices. This paper presents a hybrid localization scheme that combines received signal strength (RSS) and round-trip time (RTT) information. It is based on a RSS ranging technique that dynamically update the model that best fit the RSS information to the actual distance. RTT information, among other heuristics, are used to refine the search of that model. Once distances have been estimated, the position of the mobile station (MS) is estimated using a trilateration technique that combines the RSS and RTT ranging estimates after applying a median filter to remove outliers. By means of simulations and measurements, this paper demonstrates that combining RSS and RTT information it is possible to outperform the conventional RSS-based and RTT-based localization schemes, without using either a tracking technique or a previous calibration stage of the environment.

Self-calibration of TOA/distance relationship for wireless localization in harsh environments

The success of location-based services in open areas, mainly driven by Global Navigation Satellite Systems (GNSS), has pushed the research community towards the development of robust systems that provide a similar localization accuracy in harsh environments, where there is no line-of-sight (LOS) to satellites. Techniques based on time-of-arrival (TOA) measurements are the most promising since they can offer an appropriate balance between accuracy and complexity. However, their performance is commonly compromised by the knowledge of an accurate model relating TOA measurements and distance, which can involve an arduous task of calibration. In this paper, we present a method for real-time self-calibration of the model relating TOA and distance, based on TOA measurements exchanged among the anchors in the wireless network. Simulation and empirical results show the appropriateness of the derived models for a TOA-based localization system, since the obtained errors are remarkably close to the ones achieved with pre-calibrated parameters.

Measurement procedure to assess exposure to extremely low-frequency fields: a primary school case study

How to correctly measure the exposure of general public to extremely low-frequency (ELF) radiation is a key issue for ELF epidemiological studies. This paper proposes a measurement procedure to accurately assess the exposure of people to electric and magnetic field in the frequency band from 5 Hz to 100 kHz in buildings and their premises. As ELF radiation could be particularly harmful to children, the measurement procedure is focused on exposure to ELF in schools. Thus, the students exposure to ELF fields can be assessed by correlating the ELF measurements to the hours of school activity. In this paper, the measurement protocol was applied to study the ELF exposure on students from García Quintana primary school in Valladolid, Spain. The campaign of measurements for ELF exposure assessment in this primary school was of great interest for the Regional Council of Public Health because of the social alarm generated by the presence of a significant number cancer cases in children.

E-Field Assessment Errors Caused by the Human Body on Localization Systems

Due to their availability and cost, Received Signal Strength (RSS) based techniques are the most spread localization schemes when a person who carries an RSS meter is going to be located. However, this techniques are subject to errors associated with perturbations of the fields by the presence of the human body. Although these alterations are complex they are not completely unpredictable. This paper presents a few simple case studies to assess the E-field strength errors caused by the presence of the human body on RSS based localization schemes in a theoretical and experimental approach. It can be concluded that in a common but conservative worst case scenario this error could reach up to 15 dB and therefore it is of concern for analysis. Also, useful insights into the overall problem based on finite-difference time-domain (FDTD) simulations are given.

On the minimization of different sources of error for an RTT-based indoor localization system without any calibration stage

Prior to the trilateration process, different sources of error disturb the range estimates in any localization system, especially in dense cluttered environments where the non-line-of-sight (NLOS) between the mobile user (MU) and the access points (AP) becomes the main problem. Common error mitigation approaches are based on linear or Gaussian assumptions that are not fulfilled in this kind of scenarios, such as indoor or dense urban outdoor areas. This paper points out the better performance of a round-trip time (RTT)-based localization system when combining a prior NLOS measurements correction (PNMC) method with particle RTT-only tracking, since hard decisions are only made in the last stage of the positioning process, and neither linear nor Gaussian models are assumed. The final performance leads to a reduction of more than 57% of the error obtained without any mitigation technique, keeping the requirement of no calibration stage.