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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Exploiting Fingerprint Correlation for Fingerprint-Based Indoor Localization: A Deep Learning-Based ough fully exploiting the features of different fingerprints is to be explored as well. In this chapter, we investigate the location error of a fingerprint-based indoor localization system with the application of hybrid fingerprints. On this basis, we propose a hybrid fingerprints localization algorithm based on machine learning.

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A Scalable Framework for Indoor Localization Using Convolutional Neural Networksnatures into images, to create a scalable fingerprinting framework based on convolutional neural networks (CNNs). Our proposed CNN-based indoor localization framework (.) is validated across several indoor locales and shows improvements over the best-known prior works, with an average localization error of <2 meters.

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Smartphone Invariant Indoor Localization Using Multi-head Attention Neural Network neural network-based indoor localization framework that is resilient to device heterogeneity. An in-depth analysis of our proposed framework across a variety of indoor environments demonstrates up to 35% accuracy improvement compared to state-of-the-art indoor localization techniques.

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Book 2023dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedde

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https://doi.org/10.1007/978-3-031-26712-3Machine learning-based indoor localization; deep learning indoor localization; indoor positioning; indo

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