  Device-free Wi-Fi indoor localization has received significant attention as a
key enabling technology for many Internet of Things (IoT) applications. Machine
learning-based location estimators, such as the deep neural network (DNN),
carry proven potential in achieving high-precision localization performance by
automatically learning discriminative features from the noisy wireless signal
measurements. However, the inner workings of DNNs are not transparent and not
adequately understood especially in the indoor localization application. In
this paper, we provide quantitative and visual explanations for the DNN
learning process as well as the critical features that DNN has learned during
the process. Toward this end, we propose to use several visualization
techniques, including: 1) dimensionality reduction visualization, to project
the high-dimensional feature space to the 2D space to facilitate visualization
and interpretation, and 2) visual analytics and information visualization, to
quantify relative contributions of each feature with the proposed feature
manipulation procedures. The results provide insightful views and plausible
explanations of the DNN in device-free Wi-Fi indoor localization using channel
state information (CSI) fingerprints.
