Titlebook: Machine Learning for Indoor Localization and Navigation; Saideep Tiku,Sudeep Pasricha Book 2023 The Editor(s) (if applicable) and The Auth

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prove theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniqu978-3-031-26714-7978-3-031-26712-3

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Facundo Lezama,Federico Larroca,Germán Capdehourat

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Indoor Localization Using Trilateration and Location Fingerprinting Methodsthat in both the line-of-sight and non-line-of-sight experiments, the error is less than 0.5 meter within 3 meters in distance prediction by path loss models. The experimental results show that the trilateration localization algorithm is prone to error. The location fingerprinting-based method shows

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Fusion of WiFi and IMU Using Swarm Optimization for Indoor Localizationter, we propose a new indoor localization system that integrates the inertial sensing and RSS fingerprinting via a modified Particle Swarm Optimization (PSO)-based algorithm. Different from traditional methods, our proposed method improves the accuracy by a new optimization process, in which the Ine

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Learning Indoor Area Localization: The Trade-Off Between Expressiveness and Reliabilitycoarser location estimate (e.g., area/zone) with a higher accuracy. The size and shape of the predicted areas determine the model’s expressiveness (user gain) while influencing the degree to which the model provides a correct prediction (reliability). In this chapter we will introduce the area local

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Overview of Approaches for Device Heterogeneity Management During Indoor Localizationnsformation, calibration-free function mapping method, and non-absolute fingerprint method, respectively. The principles of the implementation for these methods are presented in this chapter. Different evaluation metrics are utilized to participate in the comparison of these methods. The advantages

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Book 2023reless signal variations, and security vulnerabilities. Countering these challenges can improve theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniqu