Localization method for device-to-device through user movement

International audienceIndoor positioning system is a key component for developing various location based services such as indoor navigation in large complex buildings (e.g., commercial center and hospital). Meanwhile, it is challenging to design a cost effective solution which is able to provide high accuracy. A new method, namely Two-Step Movement (2SM), was proposed in [1] to demonstrate how to build a positioning system which requires only one Reference Point (RP) by exploiting user movement. The method can offer good precision and minimize the number of RPs required so as to reduce system implementation cost. Built on 2SM, here we first improve the positioning performance through multi-sampling technique to combat measurement noise. Secondly, we propose the Generalized Two-Step Movement (G2SM) method for device-to-device (D2D) systems in which both the mobile terminal (MT) and RP can be mobile device. The mobile user's position can be derived analytically and given in simple closed-form expression. Its effectiveness in the presence of noise is shown in simulation results

Optimization of Algorithms in Relation to iBeacon

The boom of portable electronics and high-speed wireless networks has brought changes throughout society, including development in positioning systems. Indoor localization is more and more important. With modern technology, we are able to track people in shopping complexes and offer them discounts for surrounding goods. The following text deals with the design and description of methods to determine user’s position based on fingerprint technology. The text focuses on the description of algorithms in relation to the iBeacon. Three main algorithms were described in the text. The following text describes the implementation of Knn algorithm. The main goal of this paper is to clearly describe the basic positioning algorithms for the readers, introduce implementation of the Knn algorithm and its usage in real environment

UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis

Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m 2 . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system

Algorithms and Methods for Received Signal Strength Based Wireless Localization

In the era of wireless communications, the demand for localization and localization-based services has been continuously growing, as increasingly smarter wireless devices have emerged to the market. Besides the already available satellite-based localization systems, such as the GPS and GLONASS, also other localization approaches are needed to complement the existing solutions. Finding different types of low-cost localization methods, especially for indoors, has become one of the most important research topics in recent years.One of the most used approaches in localization is based on Received Signal Strength (RSS) information. Specific fingerprints about RSS are collected and stored and positioning can be done through pattern or feature matching algorithms or through statistical inference. A great and immediate advantage of the RSS-based localization is its ability to exploit the already existing infrastructure of different communications networks without the need to install additional system hardware. Furthermore, due to the evident connection between the RSS level and the quality of a communications signal, the RSS is usually inherently included in the network measurements. This favors the availability of the RSS measurements in the current and future wireless communications systems.In this thesis, we study the suitability of RSS for localization in various communications systems including cellular networks, wireless local area networks, personal area networks, such as WiFi, Bluetooth and Radio Frequency Identification (RFID) tags. Based on substantial real-life measurement campaigns, we study different characteristics of RSS measurements and propose several Path Loss (PL) models to capture the essential behavior of the RSS levels in 2D outdoor and 3D indoor environments. By using the PL models, we show that it is possible to attain similar performance to fingerprinting with a database size of only 1-2% of the database size needed in fingerprinting. In addition, we study the effect of different error sources, such as database calibration errors, on the localization accuracy. Moreover, we propose a novel method for studying how coverage gaps in the fingerprint database affect the localization performance. Here, by using various interpolation and extrapolation methods, we improve the localization accuracy with imperfect fingerprint databases, such as those including substantial cover-age gaps due to inaccessible parts of the buildings

Kernel and Multi-Class Classifiers for Multi-Floor WLAN Localisation

Indoor localisation techniques in multi-floor environments are emerging for location based service applications. Developing an accurate location determination and time-efficient technique is crucial for online location estimation of the multi-floor localisation system. The localisation accuracy and computational complexity of the localisation system mainly relies on the performance of the algorithms embedded with the system. Unfortunately, existing algorithms are either time-consuming or inaccurate for simultaneous determination of floor and horizontal locations in multi-floor environment. This thesis proposes an improved multi-floor localisation technique by integrating three important elements of the system; radio map fingerprint database optimisation, floor or vertical localisation, and horizontal localisation. The main focus of this work is to extend the kernel density approach and implement multi- class machine learning classifiers to improve the localisation accuracy and processing time of the each and overall elements of the proposed technique. For fingerprint database optimisation, novel access point (AP) selection algorithms which are based on variant AP selection are investigated to improve computational accuracy compared to existing AP selection algorithms such as Max-Mean and InfoGain. The variant AP selection is further improved by grouping AP based on signal distribution. In this work, two AP selection algorithms are proposed which are Max Kernel and Kernel Logistic Discriminant that implement the knowledge of kernel density estimate and logistic regression machine learning classification. For floor localisation, the strategy is based on developing the algorithm to determine the floor by utilising fingerprint clustering technique. The clustering method is based on simple signal strength clustering which sorts the signals of APs in each fingerprint according to the strongest value. Two new floor localisation algorithms namely Averaged Kernel Floor (AKF) and Kernel Logistic Floor (KLF) are studied. The former is based on modification of univariate kernel algorithm which is proposed for single-floor localisation, while the latter applies the theory kernel logistic regression which is similar to AP selection approach but for classification purpose. For horizontal localisation, different algorithm based on multi-class k-nearest neighbour classifiers with optimisation parameter is presented. Unlike the classical kNN algorithm which is a regression type algorithm, the proposed localisation algorithms utilise machine learning classification for both linear and kernel types. The multi-class classification strategy is used to ensure quick estimation of the multi-class NN algorithms. All of the algorithms are later combined to provide device location estimation for multi-floor environment. Improvement of 43.5% of within 2 meters location accuracy and reduction of 15.2 times computational time are seen as compared to existing multi-floor localisation techniques by Gansemer and Marques. The improved accuracy is due to better performance of proposed floor and horizontal localisation algorithm while the computational time is reduced due to introduction of AP selection algorithm

Physical Layer Challenges and Solutions in Seamless Positioning via GNSS, Cellular and WLAN Systems

As different positioning applications have started to be a common part of our lives, positioning methods have to cope with increasing demands. Global Navigation Satellite System (GNSS) can offer accurate location estimate outdoors, but achieving seamless large-scale indoor localization remains still a challenging topic. The requirements for simple and cost-effective indoor positioning system have led to the utilization of wireless systems already available, such as cellular networks and Wireless Local Area Network (WLAN). One common approach with the advantage of a large-scale standard-independent implementation is based on the Received Signal Strength (RSS) measurements.This thesis addresses both GNSS and non-GNSS positioning algorithms and aims to offer a compact overview of the wireless localization issues, concentrating on some of the major challenges and solutions in GNSS and RSS-based positioning. The GNSS-related challenges addressed here refer to the channel modelling part for indoor GNSS and to the acquisition part in High Sensitivity (HS)-GNSS. The RSSrelated challenges addressed here refer to the data collection and calibration, channel effects such as path loss and shadowing, and three-dimensional indoor positioning estimation.This thesis presents a measurement-based analysis of indoor channel models for GNSS signals and of path loss and shadowing models for WLAN and cellular signals. Novel low-complexity acquisition algorithms are developed for HS-GNSS. In addition, a solution to transmitter topology evaluation and database reduction solutions for large-scale mobile-centric RSS-based positioning are proposed. This thesis also studies the effect of RSS offsets in the calibration phase and various floor estimators, and offers an extensive comparison of different RSS-based positioning algorithms

Probabilistic radio-frequency fingerprinting and localization on the run

Indoor localization is a key enabler for pervasive computing and network optimization. Wireless local area network (WLAN) positioning systems typically rely on fingerprints of received signal strength (RSS) measures from access points. In this paper, we review approaches for modeling full distributions of Wi-Fi signals, including Bayesian graphical models, smoothing, compressive sensing, and random field differentiation and concentrate on the Kullback-Leibler divergence metric that compares multivariate RSS distributions. We provide theoretical insights on the required spatial density of fingerprints and on the number of samples necessary, during tracking or during signal map building, to differentiate among signal distributions and to provide accurate location estimates. We validate our methods on contrasting datasets where we obtain state-of-the-art localization results. Finally, we exploit datasets collected by a self-localizing mobile robot that continuously records Wi-Fi along with ground truth position, where we define increasingly denser fingerprint grids and study asymptotic localization accuracy.23 page(s