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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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發(fā)表于 2025-3-21 18:07:05 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Machine Learning for Indoor Localization and Navigation
編輯Saideep Tiku,Sudeep Pasricha
視頻videohttp://file.papertrans.cn/621/620623/620623.mp4
概述Provides comprehensive coverage of the application of machine learning.Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization.Covers design an
圖書(shū)封面Titlebook: Machine Learning for Indoor Localization and Navigation; Saideep Tiku,Sudeep Pasricha Book 2023 The Editor(s) (if applicable) and The Auth
描述While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense 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 embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless 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
出版日期Book 2023
關(guān)鍵詞Machine learning-based indoor localization; deep learning indoor localization; indoor positioning; indo
版次1
doihttps://doi.org/10.1007/978-3-031-26712-3
isbn_softcover978-3-031-26714-7
isbn_ebook978-3-031-26712-3
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 23:54:38 | 只看該作者
Smart Device-Based PDR Methods for Indoor Localizationon capability. Sensors embedded in these devices are relatively low-cost and convenient to carry. Consequently, leveraging the sensors embedded in smart devices has provided new opportunities for indoor PDR developments. In this chapter, we first introduce various types of smart devices and device-b
板凳
發(fā)表于 2025-3-22 04:28:44 | 只看該作者
Geometric Indoor Radiolocation: History, Trends and Open Issuesrties of the received signal, the so-called geometric radiolocation techniques. A brief reference to the localization history, actors, and architectures, along of a taxonomy of the different concepts of localization, introduces the chapter, before presenting a detailed discussion of the most common
地板
發(fā)表于 2025-3-22 07:57:42 | 只看該作者
Indoor Localization Using Trilateration and Location Fingerprinting Methodsl techniques to identify the location of the intersection point of three circles. A location “fingerprinting” algorithm is normally comprised of two stages. In the first stage, a positioning fingerprint database is established and the second stage is matching the fingerprint with the database. Kalma
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發(fā)表于 2025-3-22 19:48:13 | 只看該作者
A Scalable Framework for Indoor Localization Using Convolutional Neural Networkstals, and underground mines. Most prior works in the domain of indoor localization deliver inadequate localization accuracies without expensive infrastructure. Alternatively, methodologies employing inexpensive off-the-shelf devices that are ubiquitous in nature lack consistency in localization qual
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發(fā)表于 2025-3-23 03:47:46 | 只看該作者
Exploiting Fingerprint Correlation for Fingerprint-Based Indoor Localization: A Deep Learning-Based lying various fingerprints in improving localization accuracy still remains unknown. Moreover, how to design efficient indoor localization methods through fully exploiting the features of different fingerprints is to be explored as well. In this chapter, we investigate the location error of a finger
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發(fā)表于 2025-3-23 06:25:32 | 只看該作者
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