Deep Transfer Learning for WiFi Localization

by   Peizheng Li, et al.

This paper studies a WiFi indoor localisation technique based on using a deep learning model and its transfer strategies. We take CSI packets collected via the WiFi standard channel sounding as the training dataset and verify the CNN model on the subsets collected in three experimental environments. We achieve a localisation accuracy of 46.55 cm in an ideal (6.5m × 2.5m) office with no obstacles, 58.30 cm in an office with obstacles, and 102.8 cm in a sports hall (40 × 35m). Then, we evaluate the transfer ability of the proposed model to different environments. The experimental results show that, for a trained localisation model, feature extraction layers can be directly transferred to other models and only the fully connected layers need to be retrained to achieve the same baseline accuracy with non-transferred base models. This can save 60 time by more than half. Finally, an ablation study of the training dataset shows that, in both office and sport hall scenarios, after reusing the feature extraction layers of the base model, only 55 to obtain the models' accuracy similar to the base models.



There are no comments yet.


page 2


RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr

Fine-tuning the deep convolution neural network(CNN) using a pre-trained...

Wireless Localisation in WiFi using Novel Deep Architectures

This paper studies the indoor localisation of WiFi devices based on a co...

Transfer Learning for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is a technique to recover source ap...

An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction

Background: Pharmacokinetic evaluation is one of the key processes in dr...

Medical Multimodal Classifiers Under Scarce Data Condition

Data is one of the essential ingredients to power deep learning research...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.