To　take　advantage　of　various　types　of　dynamic　measurement　data　for　structural　damage　detection,　an　identification　strategy　based　on　cross-correlation　function　with　data　fusion　of　various　dynamic　measurements　was　proposed　to　improve　the　accuracy　of　damage　identification　in　the　present　study.　First　of　all,　the　cross-correlation　functions　among　the　easily　acquired　strain,　acceleration,　and　displacement　responses　were　theoretically　derived　when　the　structure　was　subjected　to　ambient　excitations.　Furthermore,　an　identification　strategy　was　proposed　for　structural　damage　detection　with　the　objective　function　of　minimizing　the　difference　between　the　measured　and　computed　cross-correlation　function　under　multiple　unknown　ambient　excitations.　In　the　proposed　strategy,　four　optimization　methods,　namely,　gradient　search,　genetic　algorithm,　particle　swarm　optimization,　and　the　hybrid　method　of　particle　optimization　method　and　gradient　search　were　applied　as　the　search　engine　to　identify　structural　unknown　damage　index.　Moreover,　the　performance　of　the　proposed　identification　strategy　was　examined　by　the　numerical　studies　on　a　two-dimensional　and　a　three-dimensional　truss　as　well　as　the　experimental　study　on　a　cantilever　beam.　These　results　showed　that　the　cross-correlation　function　among　different　types　of　vibration　measurements　could　significantly　improve　the　accuracy　of　the　identification　results,　meanwhile,　the　proposed　strategy　exhibited　excellent　robustness　to　the　measurement　noise.　In　addition,　the　performance　of　the　proposed　strategy　with　different　combinations　of　vibration　data　and　the　influence　of　reference　data　on　the　accuracy　of　damage　identification　results　were　further　investigated.