Wireless Localisation in WiFi using Novel Deep Architectures
Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela, Doufexi, Tim Farnham

TL;DR
This paper introduces novel deep learning architectures, including a shallow neural network and CNN/LSTM models, for indoor WiFi device localisation using channel state information, achieving high accuracy with low computational cost.
Contribution
It presents new deep neural network architectures for WiFi localisation that automatically extract features from raw CSI data, improving accuracy and efficiency over traditional methods.
Findings
Achieved around 0.5 meters localisation accuracy with three access points.
Deep models reduce data pre-processing time by 6.5 hours.
Inference time per sample is reduced to 0.1 ms.
Abstract
This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding. First, we present a novel shallow neural network (SNN) in which features are extracted from the channel state information (CSI) corresponding to WiFi subcarriers received on different antennas and used to train the model. The single-layer architecture of this localisation neural network makes it lightweight and easy-to-deploy on devices with stringent constraints on computational resources. We further investigate for localisation the use of deep learning models and design novel architectures for convolutional neural network (CNN) and long-short term memory (LSTM). We extensively evaluate these localisation algorithms for continuous tracking in indoor environments. Experimental results prove that even an SNN model, after a careful handcrafted feature extraction, can…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Speech and Audio Processing · Underwater Vehicles and Communication Systems
