Localization using Distance Geometry : Minimal Solvers and Robust Methods for Sensor Network Self-Calibration

In this thesis, we focus on the problem of estimating receiver and sender node positions given some form of distance measurements between them. This kind of localization problem has several applications, e.g., global and indoor positioning, sensor network calibration, molecular conformations, data visualization, graph embedding, and robot kinematics. More concretely, this thesis makes contributions in three different areas.First, we present a method for simultaneously registering and merging maps. The merging problem occurs when multiple maps of an area have been constructed and need to be combined into a single representation. If there are no absolute references and the maps are in different coordinate systems, they also need to be registered. In the second part, we construct robust methods for sensor network self-calibration using both Time of Arrival (TOA) and Time Difference of Arrival (TDOA) measurements. One of the difficulties is that corrupt measurements, so-called outliers, are present and should be excluded from the model fitting. To achieve this, we use hypothesis-and-test frameworks together with minimal solvers, resulting in methods that are robust to noise, outliers, and missing data. Several new minimal solvers are introduced to accommodate a range of receiver and sender configurations in 2D and 3D space. These solvers are formulated as polynomial equation systems which are solvedusing methods from algebraic geometry.In the third part, we focus specifically on the problems of trilateration and multilateration, and we present a method that approximates the Maximum Likelihood (ML) estimator for different noise distributions. The proposed approach reduces to an eigendecomposition problem for which there are good solvers. This results in a method that is faster and more numerically stable than the state-of-the-art, while still being easy to implement. Furthermore, we present a robust trilateration method that incorporates a motion model. This enables the removal of outliers in the distance measurements at the same time as drift in the motion model is canceled

Position estimation performance evaluation of a linear lateration algorithm with an SNR-based reference station selection technique

The position estimation accuracy of the multilateration system lateration algorithm depends on several factors such as the number of stations deployed, time difference of arrival (TDOA) estimation technique and the choice of reference station. In this paper, a technique to select the suitable reference station for the lateration algorithm based on received signal-to-noise (SNR) at each of the deployed stations is presented. The position estimation performance analysis of the lateration algorithm with the reference selection technique is carried out and improvement in the position estimation accuracy is determined by comparing it with the convention approach of using fixed reference station. Monte Carlo simulation results when compared with the conventional approach based on a square station configuration showed a reduction in the position estimation error of about 20%.Keywords: reference selection, lateration algorithm, signal-to-noise-ratio, position estimatio

Radar-based millimeter-Wave sensing for accurate 3D Indoor Positioning - Potentials and Challenges

The 3D nature of modern smart applications has imposed significant 3D positioning accuracy requirements, especially in indoor environments. However, a major limitation of most existing indoor localization systems is their focus on estimating positions mainly in the horizontal plane, overlooking the crucial vertical dimension. This neglect presents considerable challenges in accurately determining the 3D position of devices such as drones and individuals across multiple floors of a building let alone the cm-level accuracy that might be required in many of these applications. To tackle this issue, millimeter-wave (mmWave) positioning systems have emerged as a promising technology offering high accuracy and robustness even in complex indoor environments. This paper aims to leverage the potential of mmWave radar technology to achieve precise ranging and angling measurements presenting a comprehensive methodology for evaluating the performance of mmWave sensors in terms of measurement precision while demonstrating the 3D positioning accuracy that can be achieved. The main challenges and the respective solutions associated with the use of mmWave sensors for indoor positioning are highlighted, providing valuable insights into their potentials and suitability for practical applications

2-Dimensional position error bias analysis of an angle of arrival based target locating system

An angle of arrival (AOA) based locating system determines the location of an emitting target using its emission detected at spatially deployed ground station (GS) with an angulation algorithm. The position estimation (PE) accuracy of the system depends on several factors one of which is the approach to the development of the angulation algorithm. For passive target locating, the closed-form angulation algorithm is used and has been known to introduce bias in the PE process. In this paper, a bias analysis of the closed-form angulation algorithm is carried out to determine its percentage in the overall position mean square error (MSE). The analysis is carried out using a three-GS triangular configuration at some randomly selected unmanned aerial vehicle (UAV) drone locations. Monte Carlo simulation result based on 200 realizations shows that the bias error introduced by the angulation algorithm in the overall position MSE is about 64%. With the knowledge of the bias percentage, the actual locations of the UAV drones within the AOA-based locating system coverage can be determined

Um estudo comparativo de métodos de localização em ambientes fechados basedo em RSS

Orientador: Lucas Francisco WannerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A localização em ambientes fechados é uma aplicação de IoT importante e abrangente. Em dispositivos de baixo custo, os métodos que dependem da Força do Sinal Recebido (RSS, sigla em inglês) em pacotes de rádio para estimar a distância e a posição dos dispositivos são amplamente usados, uma vez que não requerem hardware adicional e fornecem acurácia razoável. Embora muitos métodos de localização em ambientes fechados baseada em RSS tenham sido desenvolvidos, os estudos comparativos para esses métodos geralmente se concentram principalmente na acurácia, ignorando métricas importantes, como tempo de resposta e consumo de energia. Em dispositivos com restrição de energia, em particular, a acurácia não pode ocorrer em detrimento da redução da vida útil da bateria e, portanto, maximizar a acurácia isoladamente pode não ser desejável. Neste trabalho, apresentamos um estudo comparativo de técnicas de localização baseadas em RSS implementadas em hardware comum. Comparamos nove métodos de localização em um ambiente de escritório do mundo real com forte interferência de sinal e em cômodos de uma casa com fraca interferência de sinal. Além da acurácia, apresentamos o consumo de energia e o tempo de resposta para cada método. Os resultados mostram que os métodos de fingerprinting tiveram resultados, no geral, melhores do que os de multilateração. Mas a acurácia do segundo pode ser melhorada usando um modelo mais complexo ao custo de maior consumo de energia, mas não necessariamente com maior tempo de resposta. O estudo também mostra que nós âncoras bem posicionados podem melhorar a acurácia dos métodos sem a necessidade de qualquer modificação adicional. Além disso, a seleção de subconjuntos de nós âncoras pode fornecer uma acurácia substancialmente melhor do que usar todos eles. E um bom subconjunto pode ser composto de nós âncoras bem posicionados com outros moderadamente bem posicionadosAbstract: Indoor localization is an important and wide-ranging IoT application. In low-cost devices, methods relying on the Received Signal Strength (RSS) to estimate distance and position of nodes are widely used since they require no additional hardware and provide reasonable accuracy. While many methods for RSS-based indoor localization have been developed, comparative studies for such methods have typically focused primarily on the accuracy, ignoring important metrics such as response time and energy consumption. In energy-restricted devices, in particular, localization accuracy cannot come at the expense of reducing battery lifetime, and so maximizing accuracy in isolation may not be desirable. In this work, we present a comparative study of RSS-based localization techniques implemented in commodity hardware. We compare nine localization methods in a real-world office environment with strong radio signal interference and in rooms of a house with weak interference. In addition to accuracy, we present energy consumption and response time for each method. Results show that Fingerprinting methods had overall better results than multilateration ones. But the accuracy of the second can be increased using a more complex model with the cost of more energy consumption, but not necessarily increasing the response time. The study also shows that well-placed anchor nodes can improve the methods' accuracy without the need for any additional modification. Besides, the selection of subsets of anchor nodes could provide substantially better accuracy than using all of them. And a good subset could be made up of best-placed anchor nodes with average onesMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Identifying High-Traffic Patterns in the Workplace With Radio Tomographic Imaging in 3D Wireless Sensor Networks

The rapid progress of wireless communication and embedded mircro-sensing electro-mechanical systems (MEMS) technologies has resulted in a growing confidence in the use of wireless sensor networks (WSNs) comprised of low-cost, low-power devices performing various monitoring tasks. Radio Tomographic Imaging (RTI) is a technology for localizing, tracking, and imaging device-free objects in a WSN using the change in received signal strength (RSS) of the radio links the object is obstructing. This thesis employs an experimental indoor three-dimensional (3-D) RTI network constructed of 80 wireless radios in a 100 square foot area. Experimental results are presented from a series of stationary target localization and target tracking experiments using one and two targets. Preliminary results demonstrate a 3-D RTI network can be effectively used to generate 3-D RSS-based images to extract target features such as size and height, and identify high-traffic patterns in the workplace by tracking asset movement

WiFi-based urban localisation using CNNs

IEEE Conference on Intelligent Transportation Systems - ITSC 2019, 27-30/10/2019, Auckland, Nueva Zelanda.The continuous expanding scale of WiFi deployments in metropolitan areas has made possible to find WiFi access points at almost any place in our cities. Although WiFi has been mainly used for indoor localisation, there is a growing number of research in outdoor WiFi-based localisation. This paper presents a WiFi-based localisation system that takes advantage of the huge deployment of WiFi networks in urban areas. The idea is to complement localisation in zones where the GPS coverage is low, such as urban canyons. The proposed method explores the CNNs ability to handle large amounts of data and their high accuracy with reasonable computational costs. The final objective is to develop a system able to handle the large number of access points present in urban areas while preserving high accuracy and real time requirements. The system was tested in a urban environment, improving the accuracy with respect to the state-of-the-art and being able to work in real time

Algorithmes de localisation distribués en intérieur pour les réseaux sans fil avec la technologie IEEE 802.15.4

The Internet of Things is finally blooming through diverse applications, from home automation and monitoring to health tracking and quantified-self movement. Consumers deploy more and more low-rate and low-power connected devices that provide complex services. In this scenario, positioning these intelligent objects in their environment is necessary to provide geo-localized services, as well as to optimize the network operation. However, indoor positioning of devices using only their radio interface is still very imprecise. Indoor wireless localization techniques often deduce from the Radio frequency (RF) signal attenuation the distances that separate a mobile node from a set of reference points called landmarks. The received signal strength indicator (RSSI), which reflects this attenuation, is known in the literature to be inaccurate and unreliable when it comes to distance estimation, due to the complexity of indoor radio propagation (shadowing, multi-path fading). However, it is the only metric that will certainly be available in small and inexpensive smart objects. In this thesis, we therefore seek algorithmic solutions to the following problem: is it possible to achieve a fair localization using only the RSSI readings provided by low-quality hardware? To this extent, we first study the behavior of the RSSI, as reported by real hardware like IEEE 802.15.4 sensor nodes, in several indoor environments with different sizes and configurations , including a large scale wireless sensor network. Such experimental results confirm that the relationship between RSSI and distance depends on many factors; even the battery pack attached to the devices increases attenuation. In a second step, we demonstrate that the classical log-normal shadowing propagation model is not well adapted in indoor case, because of the RSSI values dispersion and its lack of obvious correlation with distance. We propose to correct the observed inconsistencies by developing algorithms to filter irrelevant samples. Such correction is performed by biasing the classical log-normal shadowing model to take into account the effects of multipath propagation. These heuristics significantly improved RSSI-based indoor localization accuracy results. We also introduce an RSSI-based positioning approach that uses a maximum likelihood estimator conjointly with a statistical model based on machine learning. In a third step, we propose an accurate distributed and cooperative RSSI-based localization algorithm that refines the set of positions estimated by a wireless node. This algorithm is composed of two on-line steps: a local update of position¿s set based on stochastic gradient descent on each new RSSI measurement at each sensor node. Then an asynchronous communication step allowing each sensor node to merge their common local estimates and obtain the agreement of the refined estimated positions. Such consensus approach is based on both a distributed local gradient step and a pairwise gossip protocol. This enables each sensor node to refine its initial estimated position as well as to build a local map of itself and its neighboring nodes. The proposed algorithm is compared to multilateration, Multi Dimensional Scaling (i.e. MDS) with modern majorization problem and classical MDS. Simulation as well as experimental results obtained on real testbeds lead to a centimeter-level accuracy. Both landmarks and blind nodes communicate in the way that the data processing and computation are performed by each sensor node without any central computation point, tedious calibration or intervention from a human.L¿internet des objets se développe à travers diverses applications telles que la domotique, la surveillance à domicile, etc. Les consommateurs s¿intéressent à ces applications dont les objets interagissent avec des dispositifs de plus en plus petits et connectés. La localisation est une information clé pour plusieurs services ainsi que pour l¿optimisation du fonctionnement du réseau. En environnement intérieur ou confiné, elle a fait l¿objet de nombreuses études. Cependant, l¿obtention d¿une bonne précision de localisation demeure une question difficile, non résolue. Cette thèse étudie le problème de la localisation en environnement intérieur appliqué aux réseaux sans fil avec l¿utilisation unique de l¿atténuation du signal. L¿atténuation est mesurée par l¿indicateur de l¿intensité du signal reçu (RSSI). Le RSSI est connu dans la littérature comme étant imprécis et peu fiable en ce qui concerne l¿estimation de la distance, du fait de la complexité de la propagation radio en intérieur : il s¿agit des multiples trajets, le shadowing, le fading. Cependant, il est la seule métrique directement mesurable par les petits objets communicants et intelligents. Dans nos travaux, nous avons amélioré la précision des mesures du RSSI pour les rendre applicables à l¿environnement interne dans le but d¿obtenir une meilleure localisation. Nous nous sommes également intéressés à l¿implémentation et au déploiement de solutions algorithmiques relatifs au problème suivant : est-il possible d¿obtenir une meilleure précision de la localisation en utilisant uniquement les mesures de RSSI fournies par les n¿uds capteurs sans fil IEEE 802.15.4 ? Dans cette perspective, nous avons d¿abord étudié le comportement du RSSI dans plusieurs environnements intérieurs de différentes tailles et selon plusieurs configurations , y compris un réseau de capteurs sans fil à grande échelle (SensLAB). Pour expliquer les résultats des mesures, nous avons caractérisé les objets communicants que nous utilisons, les n¿uds capteurs Moteiv TMote Sky, par une série d¿expériences en chambre anéchoïque. Les résultats expérimentaux confirment que la relation entre le RSSI et la distance dépend de nombreux facteurs même si la batterie intégrée à chaque n¿ud capteur produit une atténuation. Ensuite, nous avons démontré que le modèle de propagation log-normal shadowing n¿est pas adapté en intérieur, en raison de la dispersion des valeurs de RSSI et du fait que celles-ci ne sont pas toujours dépendantes de la distance. Ces valeurs devraient être considérées séparément en fonction de l¿emplacement de chaque n¿ud capteur émetteur. Nous avons proposé des heuristiques pour corriger ces incohérences observées à savoir les effets de la propagation par trajets multiples et les valeurs aberrantes. Nos résultats expérimentaux ont confirmé que nos algorithmes améliorent significativement la précision de localisation en intérieur avec l¿utilisation unique du RSSI. Enfin, nous avons étudié et proposé un algorithme de localisation distribué, précis et coopératif qui passe à l¿échelle et peu consommateur en termes de temps de calcul. Cet algorithme d¿approximation stochastique utilise la technique du RSSI tout en respectant les caractéristiques de l¿informatique embarquée des réseaux de capteurs sans fil. Il affine l¿ensemble des positions estimées par un n¿ud capteur sans fil. Notre approche a été comparée à d¿autres algorithmes distribués de l¿état de l¿art. Les résultats issus des simulations et des expériences en environnements internes réels ont révélé une meilleure précision de la localisation de notre algorithme distribué. L¿erreur de localisation est de l¿ordre du centimètre sans aucun n¿ud ou unité centrale de traitement, ni de calibration fastidieuse ni d¿intervention humaine

Taxonomy of fundamental concepts of localization in cyber-physical and sensor networks

Localization is a fundamental task in Cyber-Physical Systems (CPS), where data is tightly coupled with the environment and the location where it is generated. The research literature on localization has reached a critical mass, and several surveys have also emerged. This review paper contributes on the state-of-the-art with the proposal of a new and holistic taxonomy of the fundamental concepts of localization in CPS, based on a comprehensive analysis of previous research works and surveys. The main objective is to pave the way towards a deep understanding of the main localization techniques, and unify their descriptions. Furthermore, this review paper provides a complete overview on the most relevant localization and geolocation techniques. Also, we present the most important metrics for measuring the accuracy of localization approaches, which is meant to be the gap between the real location and its estimate. Finally, we present open issues and research challenges pertaining to localization. We believe that this review paper will represent an important and complete reference of localization techniques in CPS for researchers and practitioners and will provide them with an added value as compared to previous surveys