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Indoor positioning systems (Wireless localization)

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Introduzione ai Sistemi di Localizzazione Indoor ("Indoor Positioning Systems") is the second issue of the series "Quaderni del Dottorato di Ricerca in Ingegneria dell'Informazione" ("Issues of the PhD course in Information Engineering") of the University of Florence, as a didactic support to the PhD course bearing the same name. This issue is designed as an introductory text for those who want to test themselves in the emerging field of locating mobile vehicles in indoor environments, where satellite positioning services are absent or unreliable. Through its four chapters, the text reviews the main localisation methods, the corresponding algorithms, and the error propagation associated with the latter.

Indoor positioning systems (Wireless localization)

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This dissertation explores the application of Gaussian processes for positioning using radio signal strength measurements. It addresses the estimation of unknown parameters in statistical signal processing, with a focus on improving positioning accuracy in indoor environments and autonomous systems. The work involves modeling the relationship between positions and radio signal strengths, employing methods such as sequential Monte Carlo, particle filtering, and smoothing. The research also establishes Cramér-Rao bounds as benchmarks for positioning accuracy and compares different positioning scenarios. Additionally, the dissertation discusses applications in sports analytics and proposes a grey-box modeling approach for analyzing forces in cross-country skiing. The intended audience includes researchers and professionals in statistical signal processing and related fields.

Indoor positioning systems (Wireless localization) --- Gaussian processes. --- Gaussian processes

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This thesis explores the development of indoor localization systems using magnetic field-aided inertial navigation. It focuses on using spatial variations in indoor magnetic fields to provide accurate positioning information without the need for pre-installed infrastructure. The research addresses three core challenges: sensor calibration, creating a magnetic field model, and designing a state estimation algorithm. The proposed system integrates a planar magnetometer array with an inertial measurement unit to capture magnetic field variations and aid navigation. The system's effectiveness is demonstrated through simulations and real-world testing, showcasing reduced position drift and improved consistency. This work contributes to advancements in indoor localization technologies, particularly for autonomous systems and robotics.

Indoor positioning systems (Wireless localization) --- Magnetic fields. --- Magnetic fields

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With this groundbreaking text, discover how wireless artificial intelligence (AI) can be used to determine position at centimeter level, sense motion and vital signs, and identify events and people. Using a highly innovative approach that employs existing wireless equipment and signal processing techniques to turn multipaths into virtual antennas, combined with the physical principle of time reversal and machine learning, it covers fundamental theory, extensive experimental results, and real practical use cases developed for products and applications. Topics explored include indoor positioning and tracking, wireless sensing and analytics, wireless power transfer and energy efficiency, 5G and next-generation communications, and the connection of large numbers of heterogeneous IoT devices of various bandwidths and capabilities. Demo videos accompanying the book online enhance understanding of these topics. Providing a unified framework for wireless AI, this is an excellent text for graduate students, researchers, and professionals working in wireless sensing, positioning, IoT, machine learning, signal processing and wireless communications.

Wireless communication systems. --- Artificial intelligence. --- Internet of things. --- Indoor positioning systems (Wireless localization)