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In this paper, we present a new location fingerprinting database comprised ofWi-Fi received signal strength (RSS) and geomagnetic field intensity measuredwith multiple devices at a multi-floor building in Xi'an Jiatong-LiverpoolUniversity, Suzhou, China. We also provide preliminary results of localizationand trajectory estimation based on convolutional neural network (CNN) and longshort-term memory (LSTM) network with this database. For localization, we mapRSS data for a reference point to an image-like, two-dimensional array and thenapply CNN which is popular in image and video analysis and recognition. Fortrajectory estimation, we use a modified random way point model to efficientlygenerate continuous step traces imitating human walking and train a stackedtwo-layer LSTM network with the generated data to remember the changing patternof geomagnetic field intensity against (x,y) coordinates. Experimental resultsdemonstrate the usefulness of our new database and the feasibility of the CNNand LSTM-based localization and trajectory estimation with the database.

XJTLUIndoorLoc: A New Fingerprinting Database for Indoor Localization and Trajectory Estimation Based on Wi-Fi RSS and Geomagnetic Field