  In this paper, we present a new location fingerprinting database comprised of
Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured
with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool
University, Suzhou, China. We also provide preliminary results of localization
and trajectory estimation based on convolutional neural network (CNN) and long
short-term memory (LSTM) network with this database. For localization, we map
RSS data for a reference point to an image-like, two-dimensional array and then
apply CNN which is popular in image and video analysis and recognition. For
trajectory estimation, we use a modified random way point model to efficiently
generate continuous step traces imitating human walking and train a stacked
two-layer LSTM network with the generated data to remember the changing pattern
of geomagnetic field intensity against (x,y) coordinates. Experimental results
demonstrate the usefulness of our new database and the feasibility of the CNN
and LSTM-based localization and trajectory estimation with the database.
