Collaborative Indoor Positioning Systems: A Systematic Review

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

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

A WiFi RSS-RTT Indoor Positioning Model Based on Dynamic Model Switching Algorithm

The advances in WiFi technology have encouraged the development of numerous indoor positioning systems. However, their performance varies significantly across different indoor environments, making it challenging in identifying the most suitable system for all scenarios. To address this challenge, we propose an algorithm that dynamically selects the most optimal WiFi positioning model for each location. Our algorithm employs a Machine Learning weighted model selection algorithm, trained on raw WiFi RSS, raw WiFi RTT data, statistical RSS & RTT measures, and Access Point line-of-sight information. We tested our algorithm in four complex indoor environments, and compared its performance to traditional WiFi indoor positioning models and state-of-the-art stacking models, demonstrating an improvement of up to 1.8 meters on average

WIFI BASED INDOOR POSITIONING - A MACHINE LEARNING APPROACH

Navigation has become much easier these days mainly due to advancement in satellite technology. The current navigation systems provide better positioning accuracy but are limited to outdoors. When it comes to the indoor spaces such as airports, shopping malls, hospitals or office buildings, to name a few, it will be challenging to get good positioning accuracy with satellite signals due to thick walls and roofs as obstacles. This gap led to a whole new area of research in the field of indoor positioning. Many researches have been conducting experiments on different technologies and successful outcomes have beenseen. Each technology providing indoor positioning capability has its own limitations. In this thesis, different radio frequency (RF) and non-radio frequency (Non-RF) technologies are discussed but focus is set on Wi-Fi for indoor positioning. A demo indoor positioning app is developed for the Technobothnia building at the University of Vaasa premises. This building is already equipped with Wi-Fi infrastructure. A floor plan of the building, radio maps and a fingerprinting database with Wi-Fi signal strength measurements is created with help of tools from HERE technology. The app provides real-time positioning and routing as a future visitor tool. With the exceeding amounts of available data, one of the highly popular fields is applying Machine Learning (ML) to data. It can be applied in many disciplines from medicine to space. In ML, algorithms learn from the data and make predictions. Due to the significant growth in various sensor technologies and computational power, large amounts of data can be stored and processed. Here, the ML approach is also taken to the indoor positioning challenge. An open-source Wi-Fi fingerprinting dataset is obtained from Tampere University and ML algorithms are applied on it for performing indoor positioning. Algorithms are trained with received signal strength (RSS) values with their respective reference coordinates and the user location can be predicted. The thesis provides a performance analysis of different algorithms suitable for future mobile implementations

사물인터넷을 위한 무선 실내 측위 알고리즘

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022.2. 김성철.실내 위치 기반 서비스는 스마트폰을 이용한 실내에서의 경로안내, 스마트 공장에서의 자원 관리, 실내 로봇의 자율주행 등 많은 분야에 접목될 수 있으며, 사물인터넷 응용에도 필수적인 기술이다. 다양한 위치 기반 서비스를 구현하기 위해서는 정확한 위치 정보가 필요하며, 적합한 거리 및 위치를 추정 기술이 핵심적이다. 야외에서는 위성항법시스템을 이용해서 위치 정보를 획득할 수 있다. 본 학위논문에서는 와이파이 기반 측위 기술에 대해 다룬다. 구체적으로, 전파의 신호 세기 및 도달 시간을 이용한 정밀한 실내 위치 추정을 위한 세 가지 기술에 대해 다룬다. 먼저, 비가시경로 환경에서의 거리 추정 정확도를 향상시켜 거리 기반 측위의 정확도를 향상시키는 하이브리드 알고리즘을 제안한다. 제안하 알고리즘은듀얼 밴드 대역의 신호세기를 감쇄량을 측정하여 거리 기반 측위 기법을 적용할 때, 거리 추정부 단계만을 데이터 기반 학습을 이용한 깊은 신경망 회귀 모델로 대체한 방안이다. 적절히 학습된 깊은 회귀 모델의 사용으로 비가시경로 환경에서 발생하는 거리 추정 오차를 효과적으로 감소시킬 수 있으며, 결과적으로 위치 추정 오차 또한 감소시켰다. 제안한 방법을 실내 광선추적 기반 모의실험으로 평가했을 때, 기존 기법들에 비해서 위치 추정 오차를 중간값을 기준으로 22.3% 이상 줄일 수 있음을 검증했다. 추가적으로, 제안한 방법은 실내에서의 AP 위치변화 등에 강인함을 확인했다. 다음으로, 본 논문에서는 비가시경로에서 단일 대역 수신신호세기를 측정했을 때 비가시경로가 많은 실내 환경에서 위치 추정 정확도를 높이기 위한 방안을 제안한다. 단일 대역 수신신호세기를 이용하는 방안은 기존에 이용되는 와이파이, 블루투스, 직비 등의 기반시설에 쉽게 적용될 수 있기 때문에 널리 이용된다. 하지만 신호 세기의 단일 경로손실 모델을 이용한 거리 추정은 상당한 오차를 지녀서 위치 추정 정확도를 감소시킨다. 이러한 문제의 원인은 단일 경로손실 모델로는 실내에서의 복잡한 전파 채널 특성을 반영하기 어렵기 때문이다. 본 연구에서는 실내 위치 추정을 위한 목적으로, 중첩된 다중 상태 경로 감쇄 모델을 새롭게 제시한다. 제안한 모델은 가시경로 및 비가시경로에서의 채널 특성을 고려하여 잠재적인 후보 상태들을 지닌다. 한 순간의 수신 신호 세기 측정치에 대해 각 기준 기지국별로 최적의 경로손실 모델 상태를 결정하는 효율적인 방안을 제시한다. 이를 위해 기지국별 경로손실모델 상태의 조합에 따른 측위 결과를 평가할 지표로서 비용함수를 정의하였다. 각 기지국별 최적의 채널 모델을 찾는데 필요한 계산 복잡도는 기지국 수의 증가에 따라 기하급수적으로 증가하는데, 유전 알고리즘을 이용한 탐색을 적용하여 계산량을 억제하였다. 실내 광선추적 모의실험을 통한 검증과 실측 결과를 이용한 검증을 진행하였으며, 제안한 방안은 실제 실내 환경에서 기존의 기법들에 비해 위치 추정 오차를 약 31% 감소시켰으며 평균적으로 1.92 m 수준의 정확도를 달성함을 확인했다. 마지막으로 FTM 프로토콜을 이용한 실내 위치 추적 알고리즘에 대해 연구하였다. 스마트폰의 내장 관성 센서와 와이파이 통신에서 제공하는 FTM 프로토콜을 통한 거리 추정을 이용하여 실내에서 사용자의 위치를 추적할 수 있다. 하지만 실내의 복잡한 다중경로 환경으로 인한 피크 검출 실패는 거리 측정치에 편향성을 유발한다. 또한 사용하는 디바이스의 종류에 따라 예상치 못한 거리 오차가 발생할 수있다. 본 논문에서는 실제 환경에서 FTM 거리 추정을 이용할 때 발생할 수 있는 오차들을 고려하고 이를 보상하는 방안에 대해 제시한다. 확장 칼만 필터와 결합하여 FTM 결과를 사전필터링 하여 이상값을 제거하고, 거리 측정치의 편향성을 제거하여 위치 추적 정확도를 향상시킨다. 실내에서의 실험 결과 제안한 알고리즘은 거치 측정치의 편향성을 약 44-65% 감소시켰으며 최종적으로 사용자의 위치를 서브미터급으로 추적할 수 있음을 검증했다.Indoor location-based services (LBS) can be combined with various applications such as indoor navigation for smartphone users, resource management in smart factories, and autonomous driving of robots. It is also indispensable for Internet of Things (IoT) applications. For various LBS, accurate location information is essential. Therefore, a proper ranging and positioning algorithm is important. For outdoors, the global navigation satellite system (GNSS) is available to provide position information. However, the GNSS is inappropriate indoors owing to the issue of the blocking of the signals from satellites. It is necessary to develop a technology that can replace GNSS in GNSS-denied environments. Among the various alternative systems, the one of promising technology is to use a Wi-Fi system that has already been applied to many commercial devices, and the infrastructure is in place in many regions. In this dissertation, Wi-Fi based indoor localization methods are presented. In the specific, I propose the three major issues related to accurate indoor localization using received signal strength (RSS) and fine timing measurement (FTM) protocol in the 802.11 standard for my dissertation topics. First, I propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. I replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error. Next, I study on positioning algorithm that considering NLOS conditions for each APs, using single band RSS measurement. The single band RSS information is widely used for indoor localization because they can be easily implemented by using existing infrastructure like Wi-Fi, Blutooth, or Zigbee. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, I develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and NLOS states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, I formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, I apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92~m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09\% over the benchmark scenario. Finally, I investigate accurate indoor tracking algorithm using FTM protocol in this dissertation. By using the FTM ranging and the built-in sensors in a smartphone, it is possible to track the user's location in indoor. However, the failure of first peak detection due to the multipath effect causes a bias in the FTM ranging results in the practical indoor environment. Additionally, the unexpected ranging error dependent on device type also degrades the indoor positioning accuracy. In this study, I considered the factors of ranging error in the FTM protocol in practical indoor environment, and proposed a method to compensate ranging error. I designed an EKF-based tracking algorithm that adaptively removes outliers from the FTM result and corrects bias to increase positioning accuracy. The experimental results verified that the proposed algorithm reduces the average ofthe ranging bias by 43-65\% in an indoor scenarios, and can achieve the sub-meter accuracy in average route mean squared error of user's position in the experiment scenarios.Abstract i Contents iv List of Tables vi List of Figures vii 1 INTRODUCTION 1 2 Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-BandWi-Fi 6 2.1 Motivation 6 2.2 Preliminary 8 2.3 System model 11 2.4 Proposed Ranging Method 13 2.5 Performance Evaluation 16 2.5.1 Ray-Tracing-Based Simulation 16 2.5.2 Analysis of the Ranging Accuracy 21 2.5.3 Analysis of the Neural Network Structure 25 2.5.4 Analysis of Positioning Accuracy 26 2.6 Summary 29 3 Genetic Algorithm for Path Loss Model Selection in Signal Strength Based Indoor Localization 31 3.1 Motivation 31 3.2 Preliminary 34 3.2.1 RSS-based Ranging Techniques 35 3.2.2 Positioning Technique 37 3.3 Proposed localization method 38 3.3.1 Localization Algorithm with Overlapped Multi-State Path Loss Model 38 3.3.2 Localization with Genetic Algorithm-Based Search 41 3.4 Performance evaluation 46 3.4.1 Numerical simulation 50 3.4.2 Experimental results 56 3.5 Summary 60 4 Indoor User Tracking with Self-calibrating Range Bias Using FTM Protocol 62 4.1 Motivation 62 4.2 Preliminary 63 4.2.1 FTM ranging 63 4.2.2 PDR-based trajectory estimation 65 4.3 EKF design for adaptive compensation of ranging bias 66 4.4 Performance evaluation 69 4.4.1 Experimental scenario 69 4.4.2 Experimental results 70 4.5 Summary 75 5 Conclusion 76 Abstract (In Korean) 89박

Sensor fusion of IMU and BLE using a well-condition triangle approach for BLE positioning

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesGPS has been a de-facto standard for outdoor positioning. For indoor positioning different systems exist. But there is no general solution to fit all situations. A popular choice among service provider is BLE-based IPS. BLE-has low cost, low power consumption, and tit is are compatible with newer smartphones. These factors make it suitable for mass market applications with an estimated market of 10 billion USD by 2020. Although, BLEbased IPS have advantages over its counterparts, it has not solved the position accuracy problem yet. More research is needed to meet the position accuracy required for indoor LBS. In this thesis, two ways for accuracy improvement were tested i) a new algorithm for BLE-based IPS was proposed and ii) fusion of BLE position estimates with IMU position estimates was implemented. The first way exploits a concept from control survey called well-conditioned triangle. Theoretically, a well-conditioned triangle is an equilateral triangle but for in practice, triangles whose angles are greater than 30° and less than 120° are considered well-conditioned. Triangles which do not satisfy well-condition are illconditioned. An estimated position has the least error if the geometry from which it is estimated satisfy well-condition. Ill-conditioned triangle should not be used for position estimation. The proposed algorithm checked for well-condition among the closest detected beacons and output estimates only when the beacons geometry satisfied well-condition. The proposed algorithm was compared with weighted centroid (WC) algorithm. Proposed algorithm did not improve on the accuracy but the variance in error was highly reduced. The second way tested was fusion of BLE and IMU using Kálmán filter. Fusion generally gives better results but a noteworthy result from fusion was that the position estimates during turns were accurate. When used separately, both BLE and IMU estimates showed errors in turns. Fusion with IMU improved the accuracy. More research is required to improve accuracy of BLE-based IPS. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 2, 1, 2 (input data, prepossessing, methods, computational environment, results)

Comparative analysis of computer-vision and BLE technology based indoor navigation systems for people with visual impairments

Background: Considerable number of indoor navigation systems has been proposed to augment people with visual impairments (VI) about their surroundings. These systems leverage several technologies, such as computer-vision, Bluetooth low energy (BLE), and other techniques to estimate the position of a user in indoor areas. Computer-vision based systems use several techniques including matching pictures, classifying captured images, recognizing visual objects or visual markers. BLE based system utilizes BLE beacons attached in the indoor areas as the source of the radio frequency signal to localize the position of the user. Methods: In this paper, we examine the performance and usability of two computer-vision based systems and BLE-based system. The first system is computer-vision based system, called CamNav that uses a trained deep learning model to recognize locations, and the second system, called QRNav, that utilizes visual markers (QR codes) to determine locations. A field test with 10 blindfolded users has been conducted while using the three navigation systems. Results: The obtained results from navigation experiment and feedback from blindfolded users show that QRNav and CamNav system is more efficient than BLE based system in terms of accuracy and usability. The error occurred in BLE based application is more than 30% compared to computer vision based systems including CamNav and QRNav. Conclusions: The developed navigation systems are able to provide reliable assistance for the participants during real time experiments. Some of the participants took minimal external assistance while moving through the junctions in the corridor areas. Computer vision technology demonstrated its superiority over BLE technology in assistive systems for people with visual impairments. - 2019 The Author(s).Scopu

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

The increasing importance of location‐aware computing and context‐dependent information has led to a growing interest in low‐cost indoor positioning with submeter accuracy. Localization algorithms can be classified into range‐based and range‐free techniques. Additionally, localization algorithms are heavily influenced by the technology and network architecture utilized. Availability, cost, reliability and accuracy of localization are the most important parameters when selecting a localization method. In this chapter, we introduce basic localization techniques, discuss how they are implemented with radio frequency devices and then characterize the localization techniques based on the network architecture, utilized technologies and application of localization. We then investigate and address localization in indoor environments where the absence of global positioning system (GPS) and the presence of unique radio propagation properties make this problem one of the most challenging topics of localization in wireless networks. In particular, we study and review the previous work for indoor localization based on radio frequency (RF) signaling (like Bluetooth‐based localization) to illustrate localization challenges and how some of them can be overcome