Search Results for author:
Found 2 papers, 0 papers with code
Indoor Localization Under Limited Measurements: A Cross-Environment Joint Semi-Supervised and Transfer Learning Approach
Moreover, with only 40% data measurements, the proposed cross-environment approach compensates for data inadequacy and replicates the localization accuracy of the conventional method, CNN, which uses 75% data measurements.
Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach
This paper presents a novel method based on a cascaded two-stage machine learning approach for highly-accurate and robust localization in indoor environments using adaptive selection and combination of RF features.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
