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　calibrationfree　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.