WiFi　fingerprinting　method　is　an　attractive　indoor　positioning　method　due　to　widely　deployed　WiFi　access　points　(APs)　and　easily　measured　received　signal　strength　(RSS).　However,　WiFi　fingerprinting　positioning　results　are　unstable　because　of　the　fluctuation　of　RSS.　Besides,　pedestrian　dead　reckoning　(PDR)　method　relying　on　inertial　sensors　has　been　widely　used　in　real-time　tracking.　Since　PDR　has　accumulated　errors　in　long　distances　tracking,　this　paper　proposes　a　hybrid　algorithm　that　integrates　PDR　approach　with　WiFi　fingerprinting　approach　to　further　improve　positioning　accuracy.　There　are　two　key　points　in　our　algorithm.　The　first　is　utilizing　dynamic　subarea　to　restrict　the　searching　region　of　WiFi　fingerprinting　method.　The　second　is　determining　particular　weights　to　fuse　the　positioning　results　of　the　above　two　approaches　according　to　the　distances　between　the　current　positioning　results　and　the　previous　hybrid　location.　Further,　we　improve　our　hybrid　algorithm　which　is　based　on　the　adjacent　estimated　positions　of　WiFi　fingerprinting　method.　Experiment　results　showed　that　the　average　errors　of　our　hybrid　algorithm　and　improved　hybrid　algorithm　were　2.22m　and　1.64m　respectively,　which　were　reduced　by　42%　and　57%　compared　with　the　pure　PDR　method.　Therefore,　the　proposed　algorithms　can　provide　stable　and　high　positioning　accuracy　in　real　environment.