Recently,　research　studies　on　Location-Based　Services　(LBSs)　based　on　networks　including　cellular　network　and　Wi-Fi　network　have　gradually　become　popular.　Received　Signal　Strength　Indicators　(RSSIs)　from　the　network　can　be　detected　and　collected　by　mobile　devices　to　estimate　the　locations　without　adopting　the　Global　Positioning　System　(GPS).　Previous　research　studies　utilized　the　RSSIs　of　only　cellular　network　or　only　Wi-Fi　network　to　estimate　location,　which　leads　to　a　two-fold　predicament　involving　error　limits　of　cellular　network-based　methods　and　environmental　constraints　of　Wi-Fi　network-based　methods.　In　addition,　accommodating　a　highly　temporal　dependence　of　RSSI　series　data,　this　paper　proposed　a　mobile　positioning　system　based　on　Gated　Recurrent　Unit　(GRU)　with　RSSIs　from　the　heterogeneous　network.　GRU　learns　the　temporal　correlation　of　RSSIs　and　the　relationship　between　RSSIs　and　GPS　coordinates　to　estimate　the　locations　of　mobile　devices.　A　large　number　of　real　experiments　have　been　carried　out　to　verify　the　performance　of　the　proposed　method,　and　experimental　results　demonstrate　that　the　proposed　method　has　lower　errors　(i.e.,　5.86　m　and　75%　of　errors　within　4　m)　compared　with　Neural　Network　(NN),　Recurrent　Neural　Network　(RNN),　and　Long　Short-Term　Memory　(LSTM).　©　2021　Junxiang　Wang　et　al.