Fingerprinting Localization

Fingerprinting localization aims to determine a location within an environment by analyzing unique signal characteristics (fingerprints) at that point, such as Wi-Fi signals or acoustic properties. Current research emphasizes improving accuracy and efficiency through deep learning models, including transformers, convolutional neural networks, and Siamese networks, often incorporating techniques like contrastive learning, meta-learning, and data augmentation to address challenges like data scarcity and environmental dynamism. This field is significant for its potential to enhance indoor navigation, asset tracking, and location-based services in various settings, particularly where GPS is unavailable or unreliable.