Where there is no GPS signal, received signal strength (RSS) from wireless network can be used for location estimation through fingerprinting; for instance, a vector of a pair of a service set identifier (SSID) and RSS for a Wi-Fi access point (AP) measured at a known location becomes its location fingerprint and a static user/device location then can be estimated by finding the closest match between its new RSS measurement and the location fingerprints in a database. This project aims at extending Wi-Fi fingerprinting technique to trajectory estimation of mobile users/devices exploiting its space/time correlations using deep neural networks (DNNs).