Wi-Fi　fingerprint-based　indoor　localization　has　recently　attracted　significant　research　interest　since　Wi-Fi　devices　are　widely　deployed　and　practical,　and　no　additional　infrastructure　is　required.　However,　the　Received　Signal　Strength　(RSS)　is　significantly　different　on　heterogeneous　devices,　and　this　difference　has　a　negative　impact　on　localization　results.　In　this　paper,　we　propose　a　multi-fingerprint　and　multi-classifier　fusion　(MFMCF)　localization　method　to　improve　the　localization　accuracy　and　solve　the　problem　of　heterogeneous　hardware.　First,　the　individual　feature　set　of　original　RSS　fingerprint,　signal　strength　difference　(SSD)　fingerprint　and　hyperbolic　location　fingerprint　(HLF)　are　fused　as　a　composite　fingerprint　set　(CFS),　and　then　the　data　dimension　is　reduced　by　linear　discriminant　analysis　(LDA).　Second,　three　representative　machine　learning　algorithms　including　K-Nearest　Neighbor　(KNN),　Support　Vector　Machine　(SVM)　and　Random　Forest　(RF)　are　selected　and　trained　to　construct　an　integrated　fusion　model.　Finally,　in　order　to　get　more　accurate　predictions,　in　the　online　phase,　a　selective　strategy　based　on　entropy　is　proposed　by　calculating　the　entropy　of　each　classifier＇s　prediction　result.　Experiments　show　that　MFMCF　is　an　effective　scheme　to　solve　the　localization　problem　of　heterogeneous　devices　and　improve　the　localization　accuracy.　©　1967-2012　IEEE.