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Found 7 papers, 1 papers with code
SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localization
Indoor localization is a critical task in many embedded applications, such as asset tracking, emergency response, and realtime navigation.
STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization
However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy.
VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization
Wi-Fi fingerprinting-based indoor localization is an emerging embedded application domain that leverages existing Wi-Fi access points (APs) in buildings to localize users with smartphones.
Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization
One such critical challenge is device heterogeneity, i. e., the variation in the RSSI signal characteristics captured across different smartphone devices.
Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices
These factors are often ignored in indoor localization frameworks and cause gradual and catastrophic degradation of localization accuracy post-deployment (over weeks and months).
CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning
GPS technology has revolutionized the way we localize and navigate outdoors.
QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices
Indoor localization services are a crucial aspect for the realization of smart cyber-physical systems within cities of the future.
