Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Investigation of Context Determination for Advanced Navigation using Smartphone Sensors

Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning, while the behaviour can contribute additional information to the navigation solution. Although many navigation and positioning techniques have been developed, no single one is capable of providing reliable and accurate positioning in all contexts. Therefore, it is necessary for a navigation system to be able to operate across different types of contexts. Context adaptive navigation offers a solution to this problem by detecting the operating contexts and adopting different positioning techniques accordingly. This study focuses on context determination with the available sensors on smartphone, through framework design, behavioural and environmental context detection, context association, comprehensive experimental tests, and system demonstration, building the foundation for a context-adaptive navigation system. In this thesis, the overall framework of context determination is first designed. Following the framework, the behavioural contexts, covering different human activities and vehicle motions, are recognised by different machine learning classifiers in hierarchy. Their classification results are further enhanced by feature selection and a connectivity dependent filter. Environmental contexts are detected from GNSS measurements. Indoor and outdoor environments are first distinguished based on the availability and strength of GNSS signals using a hidden Markov model based method. Within the model, the different levels of connections between environments are exploited as well. Then a fuzzy inference system is designed to enable the further classification of outdoor environments into urban and open-sky. As behaviours and environments are not completely independent, this study also considers context association, investigating how behaviours can be associated within environment detection. Tests in a series of multi-context scenarios have shown that the association mechanism can further improve the reliability of context detection. Finally, the proposed context determination system has been demonstrated in daily scenarios

Smart hierarchical WiFi localization system for indoors

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014En los últimos años, el número de aplicaciones para smartphones y tablets ha crecido rápidamente. Muchas de estas aplicaciones hacen uso de las capacidades de localización de estos dispositivos. Para poder proporcionar su localización, es necesario identificar la posición del usuario de forma robusta y en tiempo real. Tradicionalmente, esta localización se ha realizado mediante el uso del GPS que proporciona posicionamiento preciso en exteriores. Desafortunadamente, su baja precisión en interiores imposibilita su uso. Para proporcionar localización en interiores se utilizan diferentes tecnologías. Entre ellas, la tecnología WiFi es una de las más usadas debido a sus importantes ventajas tales como la disponibilidad de puntos de acceso WiFi en la mayoría de edificios y que medir la señal WiFi no tiene coste, incluso en redes privadas. Desafortunadamente, también tiene algunas desventajas, ya que en interiores la señal es altamente dependiente de la estructura del edificio por lo que aparecen otros efectos no deseados, como el efecto multicamino o las variaciones de pequeña escala. Además, las redes WiFi están instaladas para maximizar la conectividad sin tener en cuenta su posible uso para localización, por lo que los entornos suelen estar altamente poblados de puntos de acceso, aumentando las interferencias co-canal, que causan variaciones en el nivel de señal recibido. El objetivo de esta tesis es la localización de dispositivos móviles en interiores utilizando como única información el nivel de señal recibido de los puntos de acceso existentes en el entorno. La meta final es desarrollar un sistema de localización WiFi para dispositivos móviles, que pueda ser utilizado en cualquier entorno y por cualquier dispositivo, en tiempo real. Para alcanzar este objetivo, se propone un sistema de localización jerárquico basado en clasificadores borrosos que realizará la localización en entornos descritos topológicamente. Este sistema proporcionará una localización robusta en diferentes escenarios, prestando especial atención a los entornos grandes. Para ello, el sistema diseñado crea una partición jerárquica del entorno usando K-Means. Después, el sistema de localización se entrena utilizando diferentes algoritmos de clasificación supervisada para localizar las nuevas medidas WiFi. Finalmente, se ha diseñado un sistema probabilístico para seguir la posición del dispositivo en movimiento utilizando un filtro Bayesiano. Este sistema se ha probado en un entorno real, con varias plantas, obteniendo un error medio total por debajo de los 3 metros

지자기 지문을 이용한 비용 효율적이고 실용적인 실내 측위 시스템

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 권태경.지난 십수 년 동안 학계와 산업 영역을 막론하여 실내 측위 시스템이 널리 연구되어 왔다. 실내 측위 시스템을 설계할 때 WiFi, Bluetooth, 관성 센서와 같은 다양한 종류의 센서나 무선 인터페이스들을 활용할 수 있는데, 그 중에서도 지자기 센서에서 측정한 자기장의 패턴을 측위에 사용하는 시스템은 정확성과 안정성 측면에서 타 시스템에 비해 큰 장점을 지니고 있다. 철골 구조에 기반하여 건축된 현대 건축물들의 실내 공간에는 지자기장의 왜곡이 발생하며 이는 곧 실내의 개별 공간들에 고유하고 안정적인 지자기 패턴을 발생시킨다. 이를 실내 측위의 맥락에서는 지자기 지문이라고 정의하는데 이 지자기 지문은 사용자의 움직임, 문과 창문의 여닫힘 등과 같은 일상적인 환경 변화에 강건하며, 특히 WiFi 등 무선 신호와 비교하였을 때 높은 수준의 안정성을 보인다. 그러나 이와 같은 안정성에도 불구하고 지자기 지문을 이용하여 실내 측위 시스템을 설계할 때에는 몇가지 고려해야할 사항들이 있다. 먼저 지자기 지문은 센서 데이터의 낮은 차원 개수로 인하여 AP 개수에 따라 데이터의 차원이 수십, 수백개에 달하는 무선 신호에 비하여 구별성이 매우 낮다. 대부분의 기존 연구들은 많은 양의 연산을 수행하는 복잡한 알고리즘과 많은 센서를 이용하여 이를 극복하였다. 또 대상 공간의 지자기 지문을 수집하기 위한 사전 조사 비용과 주기적인 지자기 지문 재수집에 드는 관리 비용 역시 지자기 지문을 사용할 때 고려해야할 문제점이다. 따라서 본 논문은 이 두 가지 문제점을 해결하는 데에 초점을 맞추어 작성되었다. 먼저 사물 인터넷 (IoT, Internet of Things) 환경에서 실내 측위를 위해 지자기 지문을 활용하는 에너지 효율적인 경량 시스템을 연구하였다. BLE 인터페이스와 센서 2개(지자기 및 가속도계)만 탑재한 새로운 하드웨어 설계 방식을 제안하였으며, 이 기기는 코인 크기의 배터리를 사용할 경우 1년 동안 동작 가능하였다. 또한 최소한의 센서 데이터만을 이용하여 강건한 사용자 보행 모델과 효율적인 알고리즘을 도입한 파티클 필터 프레임워크를 제안하였다. 직접 설계한 사물인터넷 기기와 알고리즘을 적용한 결과, 본 시스템은 낮은 계산 복잡성과 높은 에너지 효율을 보여주면서 일반적인 사무실 공간에 대하여 평균 1.62m의 측위 정확도를 달성하였다. 다음으로는 크라우드소싱 방식을 활용하는 지자기 지문 기반의 실내 측위 시스템을 제안하였다. 실내 측위의 맥락에서 크라우드소싱은 명시적인 대상 공간의 사전 조사 과정 없이 지자기 지문 데이터베이스를 구성하는 방법이다. 지난 십수년 간 실내 측위 연구를 위해 크라우드소싱 방법론이 활발하게 연구되어 왔으나, 크라우드소싱에 기반한 기존 측위 시스템은 일반적으로 사전 조사 기반 시스템보다 위치 정확도가 낮게 측정되어왔다. 크라우드소싱 기반 시스텝의 낮은 측위 성능을 극복하기 위해, 본 연구에서는 지자기 기반 크라우드소싱 데이터를 사용한 실내 측위 시스템을 제안한다. 명시적인 수집 과정 없이 스마트폰 사용자가 일상생활에서 자연스럽게 지자기 지문 데이터베이스를 구축할 수 있도록 HMM 기반의 새로운 학습 모델을 구현하였으며, 주로 복도 위주로 구성된 실내 공간에서의 평가 결과, 제안된 시스템이 96.47\%의 학습 정확도와 0.25m의 중간값 측위 정확도를 달성하였다.Over the decades, indoor localization system has been widely studied in the academic and also in the industrial area. Many sensors or wireless signals such as WiFi, Bluetooth, and inertial sensors are available when designing an indoor localization system, but among them, the systems using the geomagnetic field has advantages concerning accuracy and stability. Every spatial point in an indoor space has its own distinct and stable fingerprint, which arises owing to the distortion of the magnetic field induced by the surrounding steel and iron structures. The magnetic fingerprint is robust to environmental changes like pedestrian activities and door/window movements, particularly compared with radio signals such as WiFi. This phenomenon makes many indoor positioning techniques rely on the magnetic field as an essential source of localization. Despite the robustness, there are some challenges when leveraging the magnetic fingerprint to design the indoor localization system. Due to lower discernibility of the magnetic fingerprint, most of the existing studies have exploited high computational algorithms and many sensors. Also, the cost of a site survey to collect the fingerprints and periodic management of target spaces is still problematic when using magnetic fingerprints. This dissertation thus focuses on these two challenges. First, we present an energy-efficient and lightweight system that utilizes the magnetic field for indoor positioning in the Internet of Things (IoT) environments. We propose a new hardware design of an IoT device that only has a BLE interface and two sensors (magnetometer and accelerometer), with the lifetime of one year when using a coin-size battery. We further propose an augmented particle filter framework that features a robust motion model and algorithmic-efficient localization heuristics with minimal sensory data. The prototype-based evaluation shows that the proposed system achieves a median accuracy of 1.62 m for an office building while exhibiting low computational complexity and high energy efficiency. Next, we propose a magnetic fingerprint-based indoor localization system leveraging a crowdsourcing approach. In the aspect of indoor localization, crowdsourcing is a method to construct the fingerprint database without the explicit site-survey process. Over the past decade, crowdsourcing has been actively studied for indoor localization. However, the existing localization systems based on crowdsourcing usually achieve lower location accuracy than the site survey based systems. To overcome the low performance of the crowdsourcing based approaches, we design an indoor positioning system using the crowdsourced data of the magnetic field. We substantiate a novel HMM-based learning model to construct a database of magnetic field fingerprints from smartphone users. Experiments in an indoor space consisting of aisles show that the proposed system achieves the learning accuracy of 96.47\% and median positioning accuracy of 0.25m.Chapter 1 Introduction 1p - Motivation 1p - Indoor Localization Overview 2p - Magnetic Field based Systems 5p - Organization of Dissertation 7p Chapter 2 An Energy-efficient and Lightweight Indoor Localization System for Internet-of-Things (IoT) Environments 8p - Introduction 8p - Related Work 11p - Issues on Using Magnetic Field 12p - Characteristics of Magnetic Field 12p - Variation Issues 13p - Sensing Rate 17p - Design of Energy-efficient Device for Localization 18p - Hardware Design 18p - Structure of the BLE Beacon Frame 21p - Processing Sensor Data 22p - System Architecture 25p - Overview 25p - Site Survey Methodology 25p - Particle Filter Framework 26p - Evaluation 36p - Implementation 37p - Experiment Setup 38p - Localization Performance 38p - Energy and Algorithmic Efficiency 44p - Discussion 46p Chapter 3 Magnetic Field based Indoor Localization System:A Crowdsourcing Approach 51p - Introduction 51p - Characteristics of Magnetic Field 54p - Robustness 54p - Distinctness 54p - Diversity issues 55p - Design of HMM for Crowdsoucing 56p - Basic Model 56p - Issues in HMM Learning 59p - Preliminary Experiments 59p - System Architecture 63p - Enhanced Learning Model 63p - Pre-processing Crowdsourced Data 65p - Allocating Initial HMM Parameters 67p - Comparing Similarity between the Magnetic Fingerprints 70p - Evaluation 71p - Experimental Settings 71p - Learning Accuracy 71p - Positioning Accuracy 73p - Algorithmic Efficiency 75p Chapter 4 Discussion and Future Work 76p - Open Space Issue 76p Chapter 5 Conclusion 82p Bibliography 84p 초록 92pDocto

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Indoor positioning model based on people effect and ray tracing propagation

WLAN-fingerprinting has been highlighted as the preferred technology in an Indoor Positioning System (IPS) due to its accurate positioning results and minimal infrastructure cost. However, the accuracy of IPS fingerprinting is highly influenced by the fluctuation in signal strength as a result of encountering obstacles. Many researchers have modelled static obstacles such as walls and ceilings, but hardly any have modelled the effect of people presence as an obstacle although the human body significantly impacts signal strength. Hence, the people presence effect must be considered to obtain highly accurate positioning results. Previous research proposed a model that only considered the direct path between the transmitter and the receiver. However, for indoor propagation, multipath effects such as reflection can also have a significant influence, but were not considered in past work. Therefore, this research proposes an accurate indoor positioning model that considers people presence using a ray tracing (AIRY) model in a dynamic environment which relies on existing infrastructure. Three solutions were proposed to construct AIRY: an automatic radio map using ray tracing (ARM-RT), a new human model in ray tracing (HUMORY), and a people effect constant for received signal strength indicator (RSSI) adaptation. At the offline stage, 30 RSSIs were recorded at each point using a smartphone to create a radio map database (523 points). The real-time RSSI was then compared to the radio map database at the online stage using MATLAB software to determine the user position (65 test points). The proposed model was tested at Level 3 of Razak Tower, UTM Kuala Lumpur (80 × 16 m). To test the influence of people presence, the number, position, and distance of the people around the mobile device (MD) were varied. The results showed that the closer the people were to the MD in both the Line of Sight (LOS) and Non-LOS position, the greater the decrease in RSSI, in which the increment number of people will increase the amount of reflection signals to be blocked. The signal strength reduction started from 0.5 dBm with two people and reached 0.9 dBm with seven people. In addition, the ray tracing model produced smaller errors on RSSI prediction than the multi-wall model when considering the effect of people presence. The k-nearest neighbour (KNN) algorithm was used to define the position. The initial accuracy was improved from 2.04 m to 0.57 m after people presence and multipath effects were considered. In conclusion, the proposed model successfully increased indoor positioning accuracy in a dynamic environment by overcoming the people presence effect

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

The IPIN 2019 Indoor Localisation Competition - Description and Results

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks