## 1 Introduction
Based on the fusion of high-frequency accelerometer signals and low-frequency GNSS signals, [PERSON] et al. [18] identified the modal frequencies of the Shanghai Tower; [PERSON] and [PERSON] [19] identified the modal frequencies and dynamic responses of a 40 m pedestrian steel bridge; [PERSON] et al. [20] identified the high-order modes and more accurate modal parameters of a 660 m long suspension bridge. By fusing high-frequency accelerometer signals with low-frequency strain signals, [PERSON] et al. [21] obtained displacement responses of the Canton Tower covering both ultra-low and high frequencies; [PERSON] et al. [22] identified the modal frequencies and mode shapes of a 50 m high transmission tower. In recent years, with the development of optical sensors and computer vision technology, computer vision-based displacement measurement has been widely used due to its advantages of simple installation and low cost. However, image resolution, lighting conditions, and shooting distance can affect the accuracy of vision-based measurements [23]. Accelerometers, on the other hand, can capture high-frequency, small-amplitude vibrations, supplementing the limitations of vision-based methods. Therefore, [PERSON] et al. [24] proposed the fusion of vision-based displacement with acceleration via a complementary filter and time synchronization algorithm to obtain displacement signals with reduced noise. Based on the fusion of high-frequency accelerometer signals and low-frequency vision-based displacement signals, [PERSON] et al. [25] acquired high-frequency displacement signals at a single measurement point of a steel box girder pedestrian bridge in Korea; [PERSON] et al. [26] identified modal properties of a reinforced concrete frame structure. These studies indicate that current applications of multi-sensor data fusion mainly focus on bridges, high-rise buildings, and transmission towers, while there is a lack of research on new structural types such as PV supports. This gap highlights the need for further development of multi-sensor data fusion techniques tailored to the unique challenges of PV support structures. While field modal testing provides the foundation for understanding the dynamic behavior of flexible PV supports, the absence of a unified FE modeling approach has led to significant discrepancies in wind vibration coefficients across existing studies. [PERSON] et al. [27] recommended a theoretical value of 1.73 for the wind vibration coefficient. [PERSON] et al. [28] used ANSYS for FE analysis and reported wind vibration coefficients of 2.11 and 1.98 for the along-wind and vertical displacements of flexible PV support structures, respectively. [PERSON] et al. [29] conducted wind pressure time history simulations and FE analysis, suggesting that the wind vibration coefficient for single-layer flexible PV support structures should be in the range of 1.3 to 1.7. [PERSON] et al. [30; 31; 32] simulated fluctuating wind speeds using an autoregressive model and established multi-span FE models of flexible PV supports in SAP2000, comparing the dynamic response characteristics under different wind load. Among the above studies using FE analysis, [PERSON] et al. [28] and [PERSON] et al. [29] only established single-span models without considering support columns, treating the cable ends as fixed constraints. In contrast, [PERSON], [PERSON], and [PERSON] et al. [27; 29; 30; 31; 32] considered support columns in their modeling and developed multi-span models. It can be seen that there is currently no unified approach to modeling flexible PV support structures, which further leads to significant differences in the wind vibration coefficients calculated among the aforementioned studies [28; 29; 30; 31; 32]. Therefore, it is significant to conduct the FE model updating based on field measurement of flexible PV support structures. In FE model updating, the direct method [33; 34] adjusts the model by modifying the system's stiffness, mass, and damping matrices.