Detection of UWB ranging measurement quality for collaborative indoor positioning

Wireless communication signals have become popular alternatives for indoor positioning and navigation due to lack of navigation satellite signals in such environments. The signal characteristics determine the method used for positioning as well as the positioning accuracy. Ultra-wideband (UWB) signals, with a typical bandwidth of over 1 GHz, overcome multipath problems in complicated environments. Hence, potentially achieves centimetre-level ranging accuracy in open areas. However, signals can be disrupted when placed in environments with obstructions and cause large ranging errors. This paper proposes a ranging measurement quality indicator (RQI) which detects the UWB measurement quality based on the received signal strength pattern. With a detection validity of more than 83%, the RQI is then implemented in a ranging-based collaborative positioning system. The relative constraint of the collaborative network is adjusted adaptively according to the detected RQI. The proposed detection and positioning algorithm improves positioning accuracy by 80% compared to non-adaptive collaborative positioning

A Soft Range Limited K-Nearest Neighbours Algorithm for Indoor Localization Enhancement

This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of $0.66$ m with $80\%$ of the errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, K-nearest neighbor (KNN), fingerprint-based localizatio

Sisätilapaikannusmenetelmä huonetarkkuuden parantamiseksi käyttäen staattisia radiomajakoita

Demand for indoor positioning applications has been growing lately. Indoor positioning is used for example in hospitals for patient tracking, and in airports for finding correct gates. Requirements in indoor positioning have become more strict with demands for a higher accuracy. This thesis presents a method for improving the room-level accuracy of a positioning system by using static beacons. As a static beacon, Bluetooth low energy modules will be used to test how much they can improve room-level accuracy on top of an existing positioning system. First, base technologies used in indoor positioning are reviewed. These include but not limited to: WLAN, Bluetooth and Ultra-wideband. Then, general indoor positioning are reviewed. After that, Ekahau positioning system will be introduced, as it is used as the base positioning system in the proposed method. The Ekahau positioning system applies WiFi networks and a fingerprinting method. Next, tests and their results in the thesis are shown. In the first test, a received signal strength of a static emulated WiFi AP is measured indoors to study radio signal behaviour indoors and to investigate whether the proposed method could improve the room-level accuracy. Then in the second test, the proposed method is presented and tested with the Ekahau positioning system. The results showed that it is possible to improve the room-level accuracy by using static beacons.Sisätilapaikannusta hyödyntäville sovelluksille on ollut nouseva kysyntä. Tällä hetkellä sisätilapaikannusta käytetään muun muassa sairaaloissa muistiongelmista kärsivien potilaiden seuraamiseen, ja lentokentällä oikean lentoportin löytämiseen. Sisätilapaikannussovellusten vaatimukset ovat samalla kasvaneet. Tarkkuudessa, ja eritoten huonetarkkuudessa on ollut parantamisen varaa. Tässä työssä esitetään menetelmä huonetarkkuuden parantamiseksi käyttämällä staattisia radiomajakoita. Radiomajakkana työn testeissä käytetään Bluetooth low energy -moduulia. Bluetooth low energy -moduulia testataan Ekahau-paikannusjärjestelmän kanssa, nähdäksemme parantuuko huonetarkkuus moduulia käytettäessä. Esiksi työssä esitetään teknologioita, joiden päälle monet sisätilapaikannusmenetelmät rakennetaan. Näitä ovat muun muassa WLAN, Bluetooth ja Ultrawideband. Sen jälkeen käsitellään sisätilapaikannusmenetelmiä, jotka käyttävät edellä mainittuja teknologioita. Sitten esitellään Ekahau-paikannusjärjestelmä, jota käytetään tulevissa testeissä. Ekahau-paikannusjärjestelmä käyttää hyödykseen WLAN:ia, ja perustuu menetelmään nimeltä Fingerprinting. Lopuksi esitellään työssä tehdyt testit. Ensimmäisessä testissä mitattiin emuloidun WiFi -tukiaseman lähettämien signaalien voimakkuutta sisätiloissa. Testin tarkoituksena oli tutkia radiosignaalien käyttäytymistä sisätiloissa ja sitä, kuinka hyvät mahdollisuudet työssä esitetyllä menetelmällä voi olla parantaa huonetarkkuutta. Testin tulokset olivat positiivisia. Toisessa testissä testattiin huonetarkkuutta käyttäen Bluetooth low energy moduulia Ekahaun paikannusjärjestelmän kanssa. Testiskenaarion tuloksena oli, että huonetarkkuutta onnistuttiin parantamaan käyttämällä staattisia radiomajakoita

Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings

POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe

CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

Deep learning has been widely adopted for channel state information (CSI)-fingerprinting indoor localization systems. These systems usually consist of two main parts, i.e., a positioning network that learns the mapping from high-dimensional CSI to physical locations and a tracking system that utilizes historical CSI to reduce the positioning error. This paper presents a new localization system with high accuracy and generality. On the one hand, the receptive field of the existing convolutional neural network (CNN)-based positioning networks is limited, restricting their performance as useful information in CSI is not explored thoroughly. As a solution, we propose a novel attention-augmented residual CNN to utilize the local information and global context in CSI exhaustively. On the other hand, considering the generality of a tracking system, we decouple the tracking system from the CSI environments so that one tracking system for all environments becomes possible. Specifically, we remodel the tracking problem as a denoising task and solve it with deep trajectory prior. Furthermore, we investigate how the precision difference of inertial measurement units will adversely affect the tracking performance and adopt plug-and-play to solve the precision difference problem. Experiments show the superiority of our methods over existing approaches in performance and generality improvement.Comment: 32 pages, Added references in section 2,3; Added explanations for some academic terms; Corrected typos; Added experiments in section 5, previous results unchanged; is under review for possible publicatio

Self-healing radio maps of wireless networks for indoor positioning

Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente, a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos, sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação, adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring, improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi- Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly present in industrial environments, can be explored to help build and maintain radio maps, as long as it is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018

Algorithms for Positioning with Nonlinear Measurement Models and Heavy-tailed and Asymmetric Distributed Additive Noise

Determining the unknown position of a user equipment using measurements obtained from transmitters with known locations generally results in a nonlinear measurement function. The measurement errors can have a heavy-tailed and/ or skewed distribution, and the likelihood function can be multimodal.A positioning problem with a nonlinear measurement function is often solved by a nonlinear least squares (NLS) method or, when filtering is desired, by an extended Kalman filter (EKF). However, these methods are unable to capture multiple peaks of the likelihood function and do not address heavy-tailedness or skewness. Approximating the likelihood by a Gaussian mixture (GM) and using a GM filter (GMF) solves the problem. The drawback is that the approximation requires a large number of components in the GM for a precise approximation, which makes it unsuitable for real-time positioning on small mobile devices.This thesis studies a generalised version of Gaussian mixtures, which is called GGM, to capture multiple peaks. It relaxes the GM’s restriction to non-negative component weights. The analysis shows that the GGM allows a significant reduction of the number of required Gaussian components when applied for approximating the measurement likelihood of a transmitter with an isotropic antenna, compared with the GM. Therefore, the GGM facilitates real-time positioning in small mobile devices. In tests for a cellular telephone network and for an ultra-wideband network the GGM and its filter provide significantly better positioning accuracy than the NLS and the EKF.For positioning with nonlinear measurement models, and heavytailed and skewed distributed measurement errors, an Expectation Maximisation (EM) algorithm is studied. The EM algorithm is compared with a standard NLS algorithm in simulations and tests with realistic emulated data from a long term evolution network. The EM algorithm is more robust to measurement outliers. If the errors in training and positioning data are similar distributed, then the EM algorithm yields significantly better position estimates than the NLS method. The improvement in accuracy and precision comes at the cost of moderately higher computational demand and higher vulnerability to changing patterns in the error distribution (of training and positioning data). This vulnerability is caused by the fact that the skew-t distribution (used in EM) has 4 parameters while the normal distribution (used in NLS) has only 2. Hence the skew-t yields a closer fit than the normal distribution of the pattern in the training data. However, on the downside if patterns in training and positioning data vary than the skew-t fit is not necessarily a better fit than the normal fit, which weakens the EM algorithm’s positioning accuracy and precision. This concept of reduced generalisability due to overfitting is a basic rule of machine learning.This thesis additionally shows how parameters of heavy-tailed and skewed error distributions can be fitted to training data. It furthermore gives an overview on other parametric methods for solving the positioning method, how training data is handled and summarised for them, how positioning is done by them, and how they compare with nonparametric methods. These methods are analysed by extensive tests in a wireless area network, which shows the strength and weaknesses of each method

Distributed localisation algorithm for wireless ad hoc networks of moving nodes

Existing ad hoc network localisation solutions rely either on external location references or network-wide exchange of information and centralised processing and computation of location estimates. Without these, nodes are not able to estimate the relative locations of other nodes within their communication range. This thesis defines a new distributed localisation algorithm for ad hoc networks of moving nodes. The Relative Neighbour Localisation (RNL) algorithm works without any external localisation signal or systems and does not assume centralised information processing. The idea behind the location estimates produced by the RNL algorithm is the relationship between the relative locations of two nodes, their mobility parameters and the signal strengths measured between them. The proposed algorithm makes use of the data available to each node to produce a location estimate. The signal strength each node is capable of measuring is used as one algorithm input. The other input is the velocity vector of the neighbouring node, composed of its speed and direction of movement, which each node is assumed to periodically broadcast. The relationship between the signal strength and the mobility parameters on one, and the relative location on the other side can be analytically formulated in an ideal case. The limitations of a realistic scenario complicate this relationship, making it very difficult to formulate analytically. An empirical approach is thus used. The angle and the distance estimates are individually computed, together forming a two-dimensional location estimate. The performance of the algorithm was analysed in detail using simulation, showing a median estimate error of under 10m, and its application was tested through design and evaluation of a distributed sensing coverage algorithm, showing RNL location estimates can provide 90% of the coverage achievable with true locations being known