  We implement a wireless indoor navigation system based on the variational
Gaussian process state-space model (GPSSM) with smartphone-collected WiFi
received signal strength (RSS) and inertial measurement unit (IMU) readings. We
adapt the existing variational GPSSM framework to wireless navigation
scenarios, and provide a practical learning procedure for the variational
GPSSM. The proposed system explores both the expressive power of the
non-parametric Gaussian process model and its natural mechanism for integrating
the state-of-the-art navigation techniques designed upon state-space model.
Experimental results obtained from a real office environment validate the
outstanding performance of the variational GPSSM in comparison with the
traditional parametric state-space model in terms of navigation accuracy.
