It　is　difficult　to　accurately　identify　the　dynamic　deformation　of　bridges　from　Global　Navigation　Satellite　System　(GNSS)　due　to　the　influence　of　the　multipath　effect　and　random　errors,　etc.　To　solve　this　problem,　an　improved　empirical　wavelet　transform　(EWT)-based　procedure　was　proposed　to　denoise　GNSS　data　and　identify　the　modal　parameters　of　bridge　structures.　Firstly,　the　Yule-Walker　algorithm-based　auto-power　spectrum　and　Fourier　spectrum　were　jointly　adopted　to　segment　the　frequency　bands　of　structural　dynamic　response　data.　Secondly,　the　improved　EWT　algorithm　was　used　to　decompose　and　reconstruct　the　dynamic　response　data　according　to　a　correlation　coefficient-based　criterion.　Finally,　Natural　Excitation　Technique　(NExT)　and　Hilbert　Transform　(HT)　were　applied　to　identify　the　modal　parameters　of　structures　from　the　decomposed　efficient　components.　Two　groups　of　simulation　data　were　used　to　validate　the　feasibility　and　reliability　of　the　proposed　method,　which　consisted　of　the　vibration　responses　of　a　four-storey　steel　frame　model,　and　the　acceleration　response　data　of　a　suspension　bridge.　Moreover,　field　experiments　were　carried　out　on　the　Wilford　suspension　bridge　in　Nottingham,　UK,　with　GNSS　and　an　accelerometer.　The　fundamental　frequency　(1.6707　Hz),　the　damping　ratio　(0.82%),　as　well　as　the　maximum　dynamic　displacements　(10.10　mm)　of　the　Wilford　suspension　bridge　were　detected　by　using　this　proposed　method　from　the　GNSS　measurements,　which　were　consistent　with　the　accelerometer　results.　In　conclusion,　the　analysis　revealed　that　the　improved　EWT-based　method　was　capable　of　accurately　identifying　the　low-order,　closely　spaced　modal　parameters　of　bridge　structures　under　operational　conditions.