An　angle　estimation　algorithm　for　tracking　indoor　moving　targets　with　WiFi　is　proposed.　First,　phase　calibration　and　static　path　elimination　are　proposed　and　performed　on　the　collected　channel　state　information　signals　from　different　antennas.　Then,　the　angle　of　arrival　information　is　obtained　with　the　joint　estimation　algorithm　of　the　angle　of　arrival　(AOA)　and　time　of　flight　(TOF).　To　deal　with　the　multipath　effects,　we　adopt　the　DBscan　spatiotemporal　clustering　algorithm　with　adaptive　parameters.　In　addition,　the　time-continuous　angle　of　arrival　information　is　obtained　by　interpolating　and　supplementing　points　to　extract　the　dynamic　signal　paths　better.　Finally,　the　least-squares　method　is　used　for　linear　fitting　to　obtain　the　final　angle　information　of　a　moving　target.　Experiments　are　conducted　with　the　tracking　data　set　presented　with　Tsinghua＇s　Widar　2.0.　The　results　show　that　the　average　angle　estimation　error　with　the　proposed　algorithm　is　smaller　than　Widar2.0.　The　average　angle　error　is　about　7.18　degrees　in　the　classroom　environment,　3.62　degrees　in　the　corridor　environment,　and　12.16　degrees　in　the　office　environment;　they　are　smaller　than　the　errors　of　the　existing　system.