Machine Learning for Indoor Localization Without Ground-truth Locations
Author:
Affiliation:
1. Sungshin Women’s University,Department of Future Convergence Technology Engineering,Seoul,Korea
2. Sungshin Women’s University,School of AI Convergence,Seoul,Korea
Funder
National Research Foundation of Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10332456/10332213/10332468.pdf?arnumber=10332468
Reference13 articles.
1. Robust ToA-Estimation using Convolutional Neural Networks on Randomized Channel Models
2. Two Efficient and Easy-to-Use NLOS Mitigation Solutions to Indoor 3-D AOA-Based Localization
3. Semi-supervised learning for mobile robot localization using wireless signal strengths
4. Voronoi Tessellation Based Interpolation Method for Wi-Fi Radio Map Construction
5. Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements
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