Deep Generative Model-based RSSI Synthesis for Indoor Localization
Author:
Affiliation:
1. King Mongkut’s Institute of Technology Ladkrabang,School of Engineering,Bangkok,Thailand
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9795349/9795357/09795409.pdf?arnumber=9795409
Reference14 articles.
1. Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach
2. Indoor Device-free Localization Using Received Signal Strength Indicator and Illuminance Sensor for Random-forest-based Fingerprint Technique
3. Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization
4. Indoor Fingerprint Positioning Based on Wi-Fi: An Overview
5. QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices
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1. Electromagnetic-Informed Generative Models for Passive RF Sensing and Perception of Body Motions;IEEE Open Journal of Antennas and Propagation;2024-08
2. Counteracting Packet Loss in Fingerprint-Based Indoor Positioning via Spatially Regularized Entropy and Ground-Truth Prior Variational Inference;IEEE Sensors Journal;2024-05-01
3. Millimeter Accuracy Indoor Localization System Using an Attention Convolution Model;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21
4. DeepRSSI: Generative Model for Fingerprint-Based Localization;IEEE Access;2024
5. At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence;IEEE Open Journal of the Communications Society;2024
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