NNE-Tracking: A Neural Network Enhanced Framework for Device-Free Wi-Fi Tracking

The evolution of Wi-Fi to next-generation 802.11bf demonstrates the potential of device-free Wi-Fi sensing applications, where we can remotely infer the behaviors of users without bringing into physical contact with them. Among these sensing applications, Wi-Fi tracking is critical to provide location based services. Recent Wi-Fi tracking systems can be cataloged into model-based and data-based approaches: (1) the model-based approach is to build the mathematical tracking model. However, this method is sensitive to environmental noise, and spends more execution time; (2) the data-based approach is to train a neural network. However, this method requires a lot of efforts to collect training dataset, and cannot handle all types of trajectories well. To resolve these issues, we propose the
NNE-Tracking
, a
N
eural
N
etwork
E
nhanced tracking framework. The core design principle of
NNE-Tracking
is as follows: we improve the tracking accuracy based on the data-based approach, and utilize the model-based approach to supervise whether the neural network is already working well. Moreover, we also design a framework to estimate unknown parameters of the tracking model, so that the system can automatically generate the Wi-Fi map. We take the Wi-Fi passive tracking as a specific example to explain how to apply
NNE-Tracking
in practical applications. Experimental results demonstrate that our design can reduce
$59.4\% \sim 85.3\%$
tracking errors while significantly saving execution time. As for deployment costs, we can automatically infer the Wi-Fi map without manual calibration; As for stability, when we repeat the training process with different hidden layers and random seeds, the tracking standard deviation of these neural networks is only
$\text{1.4}\,\text{cm}$
.