Relevant bibliographies by topics / Indoor positioning systems (Wireless localization)

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Academic literature on the topic 'Indoor positioning systems (Wireless localization)'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Indoor positioning systems (Wireless localization)"

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Guney, C. "RETHINKING INDOOR LOCALIZATION SOLUTIONS TOWARDS THE FUTURE OF MOBILE LOCATION-BASED SERVICES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 235–47. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-235-2017.

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Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First, GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments, existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging needs of indoor location-aware applications. Nevertheless, access to a user’s location indoors is increasingly important in the development of context-aware applications that increases business efficiency. In this study, how can the current wireless location sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in GNSS-deprived areas.
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Kim Geok, Tan, Khaing Zar Aung, Moe Sandar Aung, Min Thu Soe, Azlan Abdaziz, Chia Pao Liew, Ferdous Hossain, Chih P. Tso, and Wong Hin Yong. "Review of Indoor Positioning: Radio Wave Technology." Applied Sciences 11, no. 1 (December 30, 2020): 279. http://dx.doi.org/10.3390/app11010279.

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The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.
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Karakusak, Muhammed Zahid, Hasan Kivrak, Hasan Fehmi Ates, and Mehmet Kemal Ozdemir. "RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures." Big Data and Cognitive Computing 6, no. 3 (August 8, 2022): 84. http://dx.doi.org/10.3390/bdcc6030084.

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Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2m) under the same experiment environment.
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Chirantan Ganguly, Sagnik Nayak, S. Irene, Anil Kumar Gupta, Suresh V., and Pradeep Kumar CH. "Utilizing machine learning algorithms for localization using RSSI values of wireless LAN." ITU Journal on Future and Evolving Technologies 3, no. 2 (June 17, 2022): 98–107. http://dx.doi.org/10.52953/mvre7314.

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With the development of new technologies, there has been an upsurge in the demand for precise localization in both outdoor and indoor environments. While a Global Positioning System (GPS) provides sufficient positioning precision in outdoor settings, its accuracy declines in indoor scenarios, necessitating the development of novel positioning approaches that function accurately both indoors and outdoors. The use of various Wireless Local Area Network (WLAN) parameters for localization has been conceptualized. In this study, we attempt to do localization using machine learning methods on WLAN Received Signal Strength Indicator (WLAN RSSI) measurements. We compare the performance of multiple machine learning algorithms on the data set to see which can be used to design efficient future localization systems. The proposed study has achieved second place for the problem statement "ITU-ML5G-PS-016: Location estimation using RSSI of wireless LAN" in AI/ML in 5G Challenge 2021 organized by the International Telecommunication Union.
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Khudhair, Ahmed Azeez, Saba Qasim Jabbar, Mohammed Qasim Sulttan, and Desheng Wang. "Wireless Indoor Localization Systems and Techniques: Survey and Comparative Study." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 2 (August 1, 2016): 392. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp392-409.

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<p>The popularity, great influence and huge importance made wireless indoor localization has a unique touch, as well its wide successful on positioning and tracking systems for both human and assists also contributing to take the lead from outdoor systems in the scope of the recent research works. In this work, we will attempt to provide a survey of the existing indoor positioning solutions and attempt to classify different its techniques and systems. Five typical location predication approaches (triangulation, fingerprinting, proximity, vision analysis and trilateration) are considered here in order to analysis and provide the reader a review of the recent advances in wireless indoor localization techniques and systems to have a good understanding of state of the art technologies and motivate new research efforts in this promising direction. For these reasons, existing wireless localization position systems and location estimation schemes are reviewed. We also made a comparison among the related techniques and systems along with conclusions and future trends to identify some possible areas of enhancements. </p>
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Sung, Kwangjae, Hyung Kyu Lee, and Hwangnam Kim. "Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks." Sensors 19, no. 18 (September 10, 2019): 3907. http://dx.doi.org/10.3390/s19183907.

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The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.
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Hall, Donald L., Ram M. Narayanan, and David M. Jenkins. "SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning." Electronics 8, no. 11 (November 10, 2019): 1323. http://dx.doi.org/10.3390/electronics8111323.

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Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.
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Shubham Khunteta, Ashok Kumar Reddy Chavva, and Avani Agrawal. "AI-based indoor localization using mmWave MIMO channel at 60 GHz." ITU Journal on Future and Evolving Technologies 3, no. 2 (September 22, 2022): 243–51. http://dx.doi.org/10.52953/aorf8087.

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In recent years, indoor localization using wireless systems has been an important area of research for its applications towards health, security and the tracking of users. A Global Positioning System (GPS) is considered as the best solution for localization for outdoor scenarios but it fails to provide accurate positioning for indoor scenarios. Wi-Fi fingerprinting methods using received signal strength from multiple access points are popular for solving indoor localization problem. As the wireless systems move towards higher frequencies, higher bandwidth and a large antenna array, sensing has also become feasible along with communication, which is an important research area towards 6G named as Integrated Communication And Sensing (ISAC). ISAC relies on sensing parameter estimations, such as estimation of fine range, Doppler and angular information which contains the signature of the surrounding objects. A localization problem can be solved by analysing the sensing parameters. In this paper, we propose a solution for the localization problem for IEEE 802.11ay WLAN systems based on signal processing and Machine Learning (ML) in indoor scenarios. (...)
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Nor Hisham, Aina Nadhirah, Yin Hoe Ng, Chee Keong Tan, and David Chieng. "Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts." Data 7, no. 11 (November 9, 2022): 156. http://dx.doi.org/10.3390/data7110156.

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Indoor positioning has garnered significant interest over the last decade due to the rapidly growing demand for location-based services. As a result, a multitude of techniques has been proposed to localize objects and devices in indoor environments. Wireless fingerprinting, which leverages machine learning, has emerged as one of the most popular positioning approaches due to its low implementation cost. The prevailing fingerprinting-based positioning mainly utilizes wireless fidelity (Wi-Fi) and Bluetooth low energy (BLE) signals. However, the RSS of Wi-Fi and BLE signals are very sensitive to the layout of the indoor environment. Thus, any change in the indoor layout could potentially lead to severe degradation in terms of localization performance. To foster the development of new positioning methods, several open-source location fingerprinting datasets have been made available to the research community. Unfortunately, none of these public datasets provides the received signal strength (RSS) measurements for indoor environments with different layouts. To fill this gap, this paper presents a new hybrid Wi-Fi and BLE fingerprinting dataset for multi-floor indoor environments with different layouts to facilitate the future development of new fingerprinting-based positioning systems that can provide adaptive positioning performance in dynamic indoor environments. Additionally, the effects of indoor layout change on the location fingerprint and localization performance are also investigated.
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Farid, Zahid, Rosdiadee Nordin, Mahamod Ismail, and Nor Fadzilah Abdullah. "Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6923931.

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In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.
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Dissertations / Theses on the topic "Indoor positioning systems (Wireless localization)"

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Sehloho, Nobaene Elizabeth. "An indoor positioning system using multiple methods and tools." Thesis, Cape Peninsula University of Technology, 2015. http://hdl.handle.net/20.500.11838/2288.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2015.
Recently, the deployment and availability of wireless technology have led to the development of location and positioning services. These Location Based Services (LBSs) are attracting the attention of researchers and mobile service providers. With the importance of ubiquitous computing, the main challenge seen in the LBS is in the mobile positioning or localization within reasonable and certain accuracy. The Global Positioning System (GPS), as a widely known and used navigation system, is only appropriate for use in outdoor environments, due to the lack of line-of-sight (LOS) in satellite signals that they cannot be used accurately inside buildings and premises. Apart from GPS, Wi-Fi is among others, a widely used technology as it is an already existing infrastructure in most places. This work proposes and presents an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echo requests, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m – 3 m. The hybridization of the methods proves an enhancement in the system though improvements are still required
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Reyes, Omar Costilla. "Dynamic WIFI Fingerprinting Indoor Positioning System." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc699843/.

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A technique is proposed to improve the accuracy of indoor positioning systems based on WIFI radio-frequency signals by using dynamic access points and fingerprints (DAFs). Moreover, an indoor position system that relies solely in DAFs is proposed. The walking pattern of indoor users is classified as dynamic or static for indoor positioning purposes. I demonstrate that the performance of a conventional indoor positioning system that uses static fingerprints can be enhanced by considering dynamic fingerprints and access points. The accuracy of the system is evaluated using four positioning algorithms and two random access point selection strategies. The system facilitates the location of people where there is no wireless local area network (WLAN) infrastructure deployed or where the WLAN infrastructure has been drastically affected, for example by natural disasters. The system can be used for search and rescue operations and for expanding the coverage of an indoor positioning system.
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Tran, Huy Phuong. "Context-Aware Wi-Fi Infrastructure-based Indoor Positioning Systems." PDXScholar, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/5009.

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Large enterprises are often interested in tracking objects and people within buildings to improve resource allocation and occupant experience. Infrastructure-based indoor positioning systems (IIPS) can provide this service at low-cost by leveraging already deployed Wi-Fi infrastructure. Typically, IIPS perform localization and tracking of devices by measuring only Wi-Fi signals at wireless access points and do not rely on inertial sensor data at mobile devices (e.g., smartphones), which would require explicit user consent and sensing capabilities of the devices. Despite these advantages, building an economically viable cost-effective IIPS that can accurately and simultaneously track many devices over very large buildings is difficult due to three main challenges. First, Wi-Fi signal measurements are extremely noisy due to unpredictable multipath propagation and signal attenuation. Second, as the IIPS obtain measurements in a best effort manner without requiring any applications installed on a tracked device, the measurements are temporally sparse and non-periodic, which makes it difficult to exploit historical measurements. Third, the cost-effective IIPS have limited computational resources, in turn limiting scalability in terms of the number of simultaneously tracked devices. Prior approaches have narrowly focused on either improving the accuracy or reducing the complexity of localization algorithms. To compute the location at the current time step, they typically use only the latest explicit Wi-Fi measurements (e.g., signal strengths). The novelty of our approach lies in considering contexts of a device that can provide useful indications of the device's location. One such example of context is device motion. It indicates whether or not the device's location has changed. For a stationary device, the IIPS can either skip expensive device localization or aggregate noisy, temporally sparse location estimates to improve localization accuracy. Another example of context applicable to a moving device is a floor map that consists of pre-defined path segments that a user can take. The map can be leveraged to constrain noisy, temporally sparse location estimates on the paths. The thesis of this dissertation is that embedding context-aware capabilities in the IIPS enhances its performance in tracking many devices simultaneously and accurately. Specifically, we develop motion detection and map matching to show the benefits of leveraging two critical contexts: device motion and floor map. Providing motion detection and map matching is non-trivial in the IIPS where we must rely only on data from the Wi-Fi infrastructure. This thesis makes two contributions. First, we develop feature-based and deep learning-based motion detection models that exploit temporal patterns in Wi-Fi measurements across different access points to classify device motion in real time. Our extensive evaluations on datasets from real Wi-Fi deployments show that our motion detection models can detect device motion accurately. This, in turn, allows the IIPS to skip repeated location computation for stationary devices or improve the accuracy of localizing these devices. Second, we develop graph-based and image-based map matching models to exploit floor maps. The novelty of the graph-based approach lies in applying geometric and topological constraints to select which path segment to align the current location estimate. Our graph-based map matching can align a location estimate of a user device on the path taken by the user and close to the user's current location. The novelty of the image-based approach lies in representing for the first time, input data including location estimates and the floor map as 2D images. This novel representation enables the design, development, and application of encoder-decoder neural networks to exploit spatial relationships in input images to potentially improve location accuracy. In our evaluation, we show that the image-based approach can improve location accuracy with large simulated datasets, compared to the graph-based approach. Together, these contributions enable improvement of the IIPS in its ability to accurately and simultaneously track many devices over large buildings.
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Sakpere, Wilson Evuarherhe. "A near field communication framework for indoor navigation : design and deployment considerations." Thesis, Cape Peninsula University of Technology, 2015. http://hdl.handle.net/20.500.11838/2290.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2015.
Navigation systems are known to provide time and location information for easy and accurate navigation in a specified environment. While Global Positioning System (GPS) has recorded a considerable success for navigating outdoors, the absence of GPS indoors has made orientation in an indoor environment challenging. Furthermore, existing technologies and methods of indoor positioning and navigation, such as WLAN, Bluetooth and Infrared, have been complex, inaccurate, expensive and challenging to implement; thereby limiting the usability of these technologies in less developed countries. This limitation of navigation services makes it difficult and time consuming to locate a destination in indoor and closed spaces. Hence, recent works with Near Field Communication (NFC) has kindled interest in positioning and navigation. While navigating, users in less developed nations face several challenges, such as infrastructure complexity, high-cost solution, inaccuracy and usability. However, this research focuses on providing interventions to alleviate usability challenges, in order to strengthen the overall accuracy and the navigation effectiveness in stringent environments through the experiential manipulation of technical attributes of the positioning and navigation system in indoor environments. Therefore, this study adopted the realist ontology and the positivist epistemological approach. It followed a quantitative and experimental method of empirical enquiry, and software engineering and synthesis research methods. The study entails three implementation processes, namely map generation, positioning framework and navigation service using a prototype mobile navigation application that uses the NFC technology. It used open-source software and hardware engineering tools, instruments and technologies, such as Ubuntu Linux, Android Software Development Kit, Arduino, NFC APIs and PandaBoard. The data was collected and the findings evaluated in three stages: pre-test, experiment and post-test.
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Sagboze, Konzi Olivier. "Real-time detection of attendance at a venue using mobile devices." Thesis, Cape Peninsula University of Technology, 2017. http://hdl.handle.net/20.500.11838/2676.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2017.
The implosion of the mobile phones, mobile applications and social media in recent years has triggered a great interest for more dedicated user-generated contents. Mobile users being the focal point, these modern virtual platforms depend on and live for collecting, structuring and manipulating the very fine-grained details about users' day-to-day activities. Since every human activity takes place in a geographical context, location information ranks high among the set of data to gather about user's daily life. User's specific location details can help filter content to serve and retrieve from them. Therefore, location-based services have been developed and successfully integrated into most virtual platforms in the quest for these precious data. However, location-based services do not fulfil all requirements. They depend on a range of positioning systems which show numerous limitations. None of the existing positioning systems is perfectly accurate. Today, it is therefore difficult to pinpoint a user in a venue using location-based services. Nevertheless, with the set of existing technology and techniques, it is possible to estimate and track users’ whereabouts in real-time. Providing the best possible estimation of user's position within a given venue can help achieve better user engagement. Depending on the gap of accuracy, the end result may actually match the outcome expected from perfectly accurate positioning systems. In this work, the focus is to develop a prototype positioning system which provides the best estimation of user's position in real-time in relation to a targeted venue or location. Through a series of research and comparison study, the most suited technology and techniques are objectively selected to build the intended prototype. The challenge of indoor positioning is also addressed in this work – bearing in mind the fact that this prototype is set to work accurately and efficiently in any geographical location and structure. The prototype is evaluated according to a set of predefined standard metrics, and theories are extracted to grow knowledge about this trending topic.
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Gomes, Rui Pedro Lebreiro. "Fine-Grained localization system for indoor environments." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13534.

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Mestrado em Engenharia Electrónica e de Telecomunicações
The increasing demand for tracking solutions in indoor environments has led to the development of many indoor location systems based in the most diverse technologies. They are trying to fill a market niche left by the current available location systems such as the well-known Global Positioning System (GPS). These systems are limited to an outdoor usage due to the drastic attenuation of the GPS signals in closed areas and they cannot provide enough resolution to meet the requirements of certain applications. Therefore, it’s here proposed the conception of a system capable of locating a mobile module in indoor environments with an accuracy of a few centimeters. The system’s concept is based in measuring the time difference of arrival (TDOA) between a radio frequency signal and an ultrasonic burst in order to measure distances. The huge difference between the propagation velocities of RF waves comparatively to sound waves allows the system to accurately measure the time difference between the two arrivals and use that value to estimate the distance that separates the source from the destination. This document describes the development of all the necessary hardware for the conception of a final prototype and all the aspects regarding the software implementation. This system is composed by two types of devices that can be divided in Ultrasonic (US) transmitters and receivers. Each device is equipped with a RF module that allows them to communicate through a wireless network based in the IEEE802.15.4 protocol. In the end, a functional prototype was achieved that was subsequently submitted to several tests in order to evaluate its performance. These tests corroborated the viability of this localization method with the prototype achieving a remarkable precision level.
A crescente demanda por soluções de rastreamento em ambientes interiores levou ao desenvolvimento de vários sistemas de localização baseados nas mais diversas tecnologias. Eles vêm tentar colmatar um nicho de mercado deixado pelos sistemas de localização actualmente disponíveis como o caso do bem conhecido Sistema de Posicionamento Global (GPS). Estes sistemas estão limitados ao uso exterior devido à drástica atenuação dos sinais GPS em áreas fechadas e eles não oferecem resolução suficiente para cumprir os requisitos de certas aplicações. Por conseguinte, é aqui proposta a concepção de um sistema capaz de localizar um módulo móvel em ambientes interiores com uma resolução de alguns centímetros. O conceito do sistema é baseado na medição da diferença dos tempos de chegada entre um sinal de radiofrequência e um sinal de ultra-sons de forma a calcular distâncias. A enorme diferença entre as velocidades de propagação das ondas RF comparativamente às ondas sonoras permitem ao sistema medir com precisão a diferença entre o tempo de chegada dos dois sinais e usar esse valor para estimar a distância que separa a fonte do destino. Este documento descreve o desenvolvimento de todo o hardware necessário para a concepção de um protótipo bem como todos os aspectos relativos à implementação de software. Este sistema é composto por dois tipos de dispositivos que podem ser divididos em transmissores e receptores de sinais ultrassónicos. Cada dispositivo está equipado com um módulo de radiofrequência que lhes permite comunicar através de uma rede sem fios baseada no protocolo IEEE802.15.4. No final, foi alcançado um protótipo funcional que posteriormente foi submetido a vários testes de forma a avaliar o seu desempenho. Estes testes vieram corroborar a viabilidade deste método de localização com o protótipo a atingir um notável nível de precisão.
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Davies, C. J. "Parallel reality : tandem exploration of real and virtual environments." Thesis, University of St Andrews, 2016. http://hdl.handle.net/10023/8098.

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Alternate realities have fascinated mankind since early prehistory and with the advent of the computer and the smartphone we have seen the rise of many different categories of alternate reality that seek to augment, diminish, mix with or ultimately replace our familiar real world in order to expand our capabilities and our understanding. This thesis presents parallel reality as a new category of alternate reality which further addresses the vacancy problem that manifests in many previous alternate reality experiences. Parallel reality describes systems comprising two environments that the user may freely switch between, one real and the other virtual, both complete unto themselves. Parallel reality is framed within the larger ecosystem of previously explored alternate realities through a thorough review of existing categorisation techniques and taxonomies, leading to the introduction of the combined Milgram/Waterworth model and an extended definition of the vacancy problem for better visualising experience in alternate reality systems. Investigation into whether an existing state of the art alternate reality modality (Situated Simulations) could allow for parallel reality investigation via the Virtual Time Windows project was followed by the development of a bespoke parallel reality platform called Mirrorshades, which combined the modern virtual reality hardware of the Oculus Rift with the novel indoor positioning system of IndoorAtlas. Users were thereby granted the ability to walk through their real environment and to at any point switch their view to the equivalent vantage point within an immersive virtual environment. The benefits that such a system provides by granting users the ability to mitigate the effects of the extended vacancy problem and explore parallel real and virtual environments in tandem was experimentally shown through application to a use case within the realm of cultural heritage at a 15th century chapel. Evaluation of these user studies lead to the establishment of a number of best practice recommendations for future parallel reality endeavours.
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Moreira, Ricardo Vilaça. "Wireless-based infrastructures for indoor localization systems." Master's thesis, Universidade de Aveiro, 2010. http://hdl.handle.net/10773/7626.

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Mestrado em Engenharia Electrónica e Telecomunicações
The rapid evolution of Wireless technologies, speci cally in the area of Wireless Sensor Networks (WSN), has led to a growing demand for the availability of information anywhere, about anything. WSN are being widely used, for example, in domotic applications, partly due to their relatively low cost and their low energy consumption nature. These characteristics make this kind of networks very useful to ll some gaps in other applications, e.g. localization-related applications. This dissertation focuses, then, on the growing importance and demand for localization of people or objects and presents one of the possible approaches to the problem of indoor localization. By merging two di erent wireless technologies (IEEE 802.11 and IEEE 802.15.4), it is possible to make wireless communications more e cient for some applications. The merge is achieved by the creation of a Gateway, with support for both standards and near-transparent translation between them. This allows cheaper 802.15.4 devices to communicate with regular 802.11 TCP/IP (Transmission Control Protocol/Internet Protocol) based networks, giving space for inumerous applications, speci cally for localization purposes, which is the main goal of this project. The creation of the necessary hardware infrastructures and its respective control software, even though the solution may be used in a large range of applications, is more speci cally directed to comply with some of the requirements of the localization system to be created, eventually. So, in addition to the Gateways that make the translation between the two already referred standards, this project consists in the creation of Tags, small low-power and low-cost end-devices that are to be attached to the objects in need of localization features. These devices communicate with the Gateways by means of an 802.15.4 connection and indirectly advertise their location, by providing the signal strength of the connection. This data is computed and can be accessed in any personal computer with a web browser and a connection to the network.
A r apida evolu c~ao das tecnologias de redes sem os, em particular na area das Wireless Sensor Networks (WSN), levou ao crescimento da necessidade de obter informa c~oes, sobre qualquer coisa, em qualquer lugar. As WSN t^em sido amplamente utilizadas, por exemplo, em aplica c~oes da area da dom otica, em parte devido ao seu relativamente baixo custo e baixo consumo de energia. Estas caracter sticas fazem com que este tipo de redes seja bastante importante no preenchimento de algumas lacunas ainda existentes noutras aplica c~oes, como por exemplo, aplica c~oes de localiza c~ao de objectos. Esta disserta c~ao foca-se, ent~ao, na crescente import^ancia e necessidade de localiza c~ao de pessoas e objectos e apresenta uma das poss veis abordagens ao problema de localiza c~ao em espa cos fechados. Atrav es da integra c~ao m utua de duas tecnologias wireless diferentes (IEEE 802.11 e 802.15.4) e poss vel tornar as comunica c~oes wireless bastante mais e cientes em alguns campos de aplica c~ao. Esta integra c~ao e conseguida com a cria c~ao de um Gateway com suporte para ambos os standards referidos e tradu c~ao quase transparente (para o utilizador) entre ambos. Assim, passa a ser poss vel a comunica c~ao entre dispositivos mais baratos (802.15.4) e redes 802.11 baseadas em TCP/IP (Transmission Control Protocol/Internet Protocol), abrindo algum espa co a cria c~ao de in umeras aplica c~oes, mais especi camente, aplica c~oes relacionadas com localiza c~ao, que representam, no fundo, o objectivo principal deste projecto. A cria c~ao das infraestruturas necess arias e do respectivo software de controlo, ainda que esta solu c~ao possa ser aplicada em muitos outros campos, foi direccionada especi camente para cumprir com alguns dos requisitos do sistema de localiza c~ao que venha eventualmente a ser criado. Assim, para al em dos Gateways, que fazem a tradu c~ao entre os dois standards j a mencionados, este projecto consiste na cria c~ao de Tags, pequenos dispositivos terminais de muito baixo custo e muito baixo consumo energ etico, que poder~ao ser acoplados aos objectos que necessitem de localiza c~ao. Estes dispositivos comunicam com os Gate- ways atrav es de uma liga c~ao wireless usando o standard IEEE 802.15.4 e, indirectamente, anunciam a sua localiza c~ao atrav es da disponibiliza c~ao do valor da for ca do sinal na conex~ao. Estas informa c~oes s~ao processadas e podem ser acedidas a partir de qualquer computador pessoal que esteja munido de um browser web e ligado a mesma rede.
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Pedrera, Rubio Ferran. "Cooperative localization algorithms in ultra-wideband systems for indoor positioning." Thesis, KTH, Signalbehandling, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102358.

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Indoor positioning has become an important research field in recent years. This is due to the potential services or products that can be offered, which would be very useful for users in several applications, and the interesting commercialization possibilities. But nowadays the most developed positioning systems are used outdoors, and for example GNSSs do not provide indoor coverage, so the efforts are focused on other local radio technologies. In particular, ultra-wideband is the object of this thesis. In this thesis a set of cooperative algorithms are presented and intended for use in the localization phase of an ultra-wideband positioning system. Different approaches and points of view are shown in the literature related to the localization problem. Here there is a revision of the main concepts as well as some software implementations and their results in MATLAB, always trying to evaluate which is the best option in each situation. The Ultra-Wideband system considered in this thesis consists of a set of nodes, some of which have unknown positions that have to be estimated by the algorithms. These nodes have the ability of calculating distances between each other by measuring the round-triptime of ultra-wideband pulse signals. The distances are used as inputs to the algorithms to calculate the estimated positions of the nodes as outputs. The most remarkable detail is the cooperation, which means that the distances between any pair of nodes is used even when both have unknown positions, taking advantage of that information. The results show that using the cooperation, the algorithms can be very precise even in very demanding conditions when nodes can not communicate with the whole network due to a range limitation of the technology or physical obstacles. Therefore, cooperative algorithms represent a way to further investigate and improve the solutions for indoor positioning.
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Cheng, King-yip, and 鄭勁業. "Localization in wireless sensor networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38700189.

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Books on the topic "Indoor positioning systems (Wireless localization)"

1

Mautz, Rainer. Indoor positioning technologies. Zürich: Schweizerische Geodätische Kommission, 2012.

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1974-, Zou Wei, ed. Shi nei yi dong shi fu wu ji qi ren de gan zhi, ding wei yu kong zhi. Beijing: Ke xue chu ban she, 2008.

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Armin, Dammann, and Mensing Christian, eds. Positioning in wireless communications systems. Chichester, West Sussex: Wiley, 2014.

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Indoor Wayfinding and Navigation. Taylor & Francis Group, 2015.

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Karimi, Hassan A. Indoor Wayfinding and Navigation. Taylor & Francis Group, 2015.

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Karimi, Hassan A. Indoor Wayfinding and Navigation. Taylor & Francis Group, 2019.

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Karimi, Hassan A. Indoor Wayfinding and Navigation. Taylor & Francis Group, 2015.

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Karimi, Hassan A. Indoor Wayfinding and Navigation. Taylor & Francis Group, 2015.

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Indoor Location Technologies. Springer, 2012.

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Yang, Zheng, Yunhao Liu, and Chenshu Wu. Wireless Indoor Localization: A Crowdsourcing Approach. Springer, 2019.

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Book chapters on the topic "Indoor positioning systems (Wireless localization)"

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Sand, Stephan. "Hybridization with Localization Information from Wireless Communications Systems." In GALILEO Positioning Technology, 169–208. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-1830-2_8.

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Xiao, Yalong, Jianxin Wang, Shigeng Zhang, Haodong Wang, and Jiannong Cao. "Accurate Indoor Localization with Multiple Feature Fusion." In Wireless Algorithms, Systems, and Applications, 522–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60033-8_45.

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Huang, Wei-qing, Chang Ding, Si-ye Wang, Junyu Lin, Shao-yi Zhu, and Yue Cui. "Design and Realization of an Indoor Positioning Algorithm Based on Differential Positioning Method." In Wireless Algorithms, Systems, and Applications, 546–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60033-8_47.

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Xu, Bo, Xiaorong Zhu, and Hongbo Zhu. "RSSI-Fading-Based Localization Approach in BLE5.0 Indoor Environments." In Wireless and Satellite Systems, 131–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19156-6_13.

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Yuan, Hui, Mu Zhou, Zhian Deng, Liangbo Xie, Yong Wang, and Xiaolong Yang. "Rough Set Reduction Aided Cost-Efficient Indoor WLAN Localization." In Wireless and Satellite Systems, 194–200. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19153-5_19.

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Cong, Haifeng, Liangbo Xie, and Mu Zhou. "An Adaptive Fingerprint Database Updating Scheme for Indoor Bluetooth Positioning." In Wireless and Satellite Systems, 141–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19153-5_13.

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Filonenko, Viacheslav, Charlie Cullen, and James D. Carswell. "Asynchronous Ultrasonic Trilateration for Indoor Positioning of Mobile Phones." In Web and Wireless Geographical Information Systems, 33–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29247-7_4.

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Radaelli, Laura, Yael Moses, and Christian S. Jensen. "Using Cameras to Improve Wi-Fi Based Indoor Positioning." In Web and Wireless Geographical Information Systems, 166–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55334-9_11.

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Bin Waheed, M. Hamza, Rao Naveed Bin Rais, Hassan Khan, Mukhtiar Bano, and Syed Sherjeel A. Gilani. "Indoor Mobile Localization Using Wireless Sensor Networks (WSNs)." In Advances in Intelligent Systems and Computing, 341–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44038-1_31.

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Yong, Guofu, Zhuoran Cai, and Hao Dong. "A High Precision Indoor Cooperative Localization Scheme Based on UWB Signals." In Wireless and Satellite Systems, 628–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19156-6_59.

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Conference papers on the topic "Indoor positioning systems (Wireless localization)"

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Mikov, Aleksandr, Alex Moschevikin, Alexander Fedorov, and Axel Sikora. "A localization system using inertial measurement units from wireless commercial hand-held devices." In 2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2013. http://dx.doi.org/10.1109/ipin.2013.6817924.

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Dobrilovic, D., Z. Stojanov, J. Stojanov, and M. Malic. "Tools for modelling distance estimation based on RSSI." In The International Workshop on Information, Computation, and Control Systems for Distributed Environments. Crossref, 2020. http://dx.doi.org/10.47350/iccs-de.2020.04.

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The systems for localization of resources in indoor environments based on Received Signal Strength Indicator (RSSI) are widely used today. Since satellite navigation systems, such as GPS or GLONASS, have certain difficulties in the indoor environments, the signals of deployed wireless devices, such as sensor nodes, access points etc, are used for localization instead. Those systems are known as Indoor Positioning System (IPS). Those systems are used for resource tracking and positioning in places such as airports, railway stations, shopping malls, warehouses, production facilities, construction sites, and healthcare institutions. The Bluetooth Low Energy is one of the wireless technologies that can be used with great efficiency for indoor localization. It offers easy and economic implementation on mobile devices such as smart phones and tablets. There are many techniques used for determination of position. In a number of methods, such as ROCRSSI or MinMax, the distance from the wireless nodes is used for calculating the location. In those systems the main challenge is to accurately estimate distance from the device based on signal strength. In this paper, usability of various software tools for modelling the distance estimation based on RSSI is discussed. Those software tools are Microsoft Access, R Studio, Octave, and Python.
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Sbirna, Sebastian, and Liana-Simona Sbirna. "Optimization of indoor localization of Automated Guided Vehicles using ultra-wideband wireless positioning sensors." In 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2019. http://dx.doi.org/10.1109/icstcc.2019.8885684.

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AL-Qutami, Tareq Aziz, and Fatin Awina Awis. "Personnel Real Time Tracking in Hazardous Areas Using Wearable Technologies and Machine Learning." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21426-ms.

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Abstract Real-time location information is essential in the hazardous process and construction areas for safety and emergency management, security, search and rescue, and even productivity tracking. It's also crucial during pandemics such as the COVID-19 pandemic for contact tracing to isolate those who came to the proximity of infected individuals. While global positioning systems (GPS), can address the demand for location awareness in outdoor environments, another accurate location estimation technology for indoor environments where GPS doesn't perform well is required. This paper presents the development and deployment of an end-to-end cost-effective real-time personnel location system suitable for both indoor and outdoor hazardous and safe areas. It leverages on facility wireless communication systems, wearable technologies such as smart helmets and wearable tags, and machine learning. Personnel carries the client device which collects location-related information and sends it to the localization algorithm in the cloud. When the personnel moves, the tracking dashboard shows client location in real-time. The proposed localization algorithm relies on wireless signal fingerprinting and machine learning algorithms to estimate the location. The machine learning algorithm is a mix of clustering and classification that was designed to scale well with bigger target areas and is suitable for cloud deployment. The system was tested in both office and industrial process environments using consumer-grade handphones and intrinsically safe wearable devices. It achieved an average distance error of less than 2 meters in 3D space.
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Nguyen, Thu L. N., and Yoan Shin. "Indoor wireless localization via convex feasibility problem." In 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2014. http://dx.doi.org/10.1109/ipin.2014.7275479.

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Bahillo, A., S. Mazuelas, J. Prieto, P. Fernandez, R. M. Lorenzo, and E. J. Abril. "Hybrid RSS-RTT localization scheme for wireless networks." In 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2010. http://dx.doi.org/10.1109/ipin.2010.5647108.

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Radoi, Ion Emilian, Dumitru Cirimpei, and Valentin Radu. "Localization Systems Repository: A Platform for Open-source Localization Systems and Datasets." In 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2019. http://dx.doi.org/10.1109/ipin.2019.8911748.

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Blywis, Bastian, Mesut Gunes, Felix Juraschek, and Steffen Gliech. "A Localization Framework for wireless mesh networks - Anchor-Free Distributed Localization in the DES-Testbed." In 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2010. http://dx.doi.org/10.1109/ipin.2010.5647378.

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Graefenstein, Juergen, Amos Albert, Peter Biber, and Andreas Schilling. "Simultaneous mobile robot and radio node localization in wireless networks." In 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2010. http://dx.doi.org/10.1109/ipin.2010.5647573.

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Jankowski, Tomasz, and Maciej Nikodem. "Synchronization-free TDoA localization method for large scale wireless networks." In 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2017. http://dx.doi.org/10.1109/ipin.2017.8115890.

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