Search Results for author:
Found 5 papers, 1 papers with code
SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning
Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted.
BIG-bench Machine Learning
Generative Adversarial Network
+1
Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets
We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset.
Towards Accelerated Localization Performance Across Indoor Positioning Datasets
As a result, we bring forward the best-performing combination of models in terms of overall positioning accuracy and processing speed and evaluate on independent sets of samples.
Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification
Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment.
Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets
The evaluation of Indoor Positioning Systems (IPS) mostly relies on local deployments in the researchers' or partners' facilities.
