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Total Size:
10.5 MB
Info Hash:
B14C1BFCFFF255E013E1D330F6A9BCA147337D63
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Added:
Dec. 8, 2025, 9:45 a.m.
Stats:
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(Last updated: Dec. 8, 2025, 9:47 a.m.)
| File | Size |
|---|---|
| Hsu L. Principles of Indoor Positioning and Indoor Navigation 2025.pdf | 10.5 MB |
Name
DL
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10.5 MB
[56
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14]
2025-12-08
| Uploaded by andryold1 | Size 10.5 MB | Health [ 56 /14 ] | Added 2025-12-08 |
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4.7 MB
[34
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10]
2023-07-01
| Uploaded by indexFroggy | Size 4.7 MB | Health [ 34 /10 ] | Added 2023-07-01 |
NOTE
SOURCE: Hsu L. Principles of Indoor Positioning and Indoor Navigation 2025
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COVER
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MEDIAINFO
Textbook in PDF format
The book is the definitive guide to mastering the algorithms, architectures, and real-world challenges behind today’s most advanced Indoor Positioning and Navigation (IPIN) systems. This comprehensive resource equips professionals with the essential tools to design accurate, reliable, and scalable indoor localization solutions. It covers the full landscape of sensing technologies, from radio frequency and physical sensors to inertial and environmental inputs, helping readers select the right positioning system for any application.
Core spatial concepts such as coordinate systems, attitude representation, and sensor calibration are addressed early on, providing the foundation needed to build accurate, high-performance systems. Dive deep into the estimation and filtering algorithms that drive indoor navigation, including least squares methods, Kalman and particle filters, and advanced factor graph optimization, with a direct comparison of their performance. The book moves into actionable techniques like time-synchronized radio positioning, differential range-based methods, fingerprinting, deep learning for feature matching, and pedestrian dead-reckoning with proprioceptive sensors. With open-source code and curated datasets, it simplifies prototype SLAM algorithms (LiDAR, Visual, and IMU-assisted), fine-tune sensor fusion strategies, and tackling real-world challenges like drift correction and temporal calibration.
This is an essential asset for engineers, researchers, and developers designing modern IPIN platforms. It provides expert insight into advanced techniques like collaborative positioning and crowdsourced mapping, which can elevate system accuracy in dense environments. Further explorations in human pose estimation, AI-driven uncertainty modeling, and reconfigurable intelligent surfaces provide a strong basis for building next-generation navigation architectures for robotics, smart buildings, industrial automation, and more. Solve key problems in the field by enabling the design of accurate and scalable indoor localization solutions
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