WiFi　fingerprinting-based　indoor　positioning　system　(IPS)　has　become　the　most　promising　solution　for　indoor　localization.　However,　there　are　two　major　drawbacks　that　hamper　its　large-scale　implementation.　First,　an　offline　site　survey　process　is　required　which　is　extremely　time-consuming　and　labor-intensive.　Second,　the　RSS　fingerprint　database　built　offline　is　vulnerable　to　environmental　dynamics.　To　address　these　issues　comprehensively,　in　this　paper,　we　propose　WinIPS,　a　WiFi-based　non-intrusive　IPS　that　enables　automatic　online　radio　map　construction　and　adaptation,　aiming　for　calibration-free　indoor　localization.　WinIPS　can　capture　data　packets　transmitted　in　existing　WiFi　traffic　and　extract　the　RSS　and　MAC　addresses　of　both　WiFi　access　points　(APs)　and　mobile　devices　in　a　non-intrusive　manner.　APs　can　be　used　as　online　reference　points　for　radio　map　construction.　A　novel　Gaussian　process　regression　model　is　proposed　to　approximate　the　non-uniform　RSS　distribution　of　an　indoor　environment.　Extensive　experiments　were　conducted,　which　demonstrated　that　WinIPS　outperforms　existing　solutions　in　terms　of　both　RSS　estimation　accuracy　and　localization　accuracy.　©　2017　IEEE.