The　unavailability　of　excitation　measurements　poses　challenges　of　application　of　many　structural　identification　methods　due　to　dealing　with　two　typical　types　of　inverse　problems　of　parameter　and　force　identification　simultaneously.　To　address　this　issue,　four　different　identification　methods　are　proposed　based　on　correlation　function　to　identify　structures　subjected　to　multiple　unknown　ambient　excitations,　namely　gradient　search,　genetic　algorithm,　particle　swarm　optimization　(PSO),　and　effective　combination　of　PSO　and　gradient　search.　Numerical　studies　on　a　cantilever　beam　and　an　eight-story　frame,　experiments　verification　on　the　ASCE　benchmark　frame　are　carried　out　to　test　the　performance　of　proposed　methods.　In　addition,　effect　of　selection　of　the　reference　point,　number　of　data　points,　unknown　initial　conditions　and　modelling　errors　on　accuracy　of　identification　results　are　also　investigated.　The　numerical　and　experimental　results　show　that　the　proposed　methods　are　capable　of　accurately　identifying　the　unknown　structural　parameters.　In　particular,　the　hybrid　method　of　PSO　and　gradient　search,　with　approach　of　producing　solutions　close　to　the　optimal　by　PSO　and　then　taking　as　initial　values　in　gradient　search　to　quickly　identify　structural　unknown　parameters,　achieves　the　best　performance　for　overall　consideration　of　identification　accuracy　and　computational　efficiency.