## 1 Introduction  
Nowadays, with the development of new materials and high-tech solutions, an increasing number of long-span bridges have emerged. Due to the influences of traffic, wind, corrosion, tidal currents, and the combination of these, these bridges easily succumb to large deformation or even destruction. Natural frequency, mode shape, and damping ratio are basic dynamic characteristics of civil engineering structures, which can reflect structures' security and stability and are worthy of further research. Recent developments in field measurement techniques have provided rapid acquisition of data and highly reliable results for analyzing structural dynamic responses. Accelerometer monitoring is an effective means with which to monitor the dynamic characteristics of long-span bridges [1; 2]. However, it inevitably causes drift errors as it requires a double integral for acquiring deformation displacement [3; 4]. Global positioning system (GPS) has been proven a useful means with which to monitor structural dynamic displacement, which is especially suitable for large scale structures with large deformation and low frequency [5; 6; 7]. The authors of this paper have performed an in-depth study on this topic [8; 9; 10]. GPS real-time kinematic (RTK) is the primary method used for monitoring structural continuous dynamic deformation [11]. Compared to GPS-RTK, multiconstellation GNSS-RTK can accept GPS, GLONASS, and BeiDou navigation satellite system (BDS) satellite signals at the same time. Its number of visible satellites of the multiconstellation GNSS system is significantly increased, and the position dilution of precision (PDOP) value is clearly decreased [12]. Considering the advantages of the multiconstellation GNSS-RTK system, it was adopted to monitor the dynamic deformation of a long-span concrete filled steel tubular arch bridge in this study. There are two critical factors in the large-scale structural health monitoring field. One is the selection of sensors and the other is the signal processing method. It has been noted that the positioning accuracies of RTK-GNSS sensors are limited, i.e., in the horizontal direction: \(\pm 1\) cm; in the vertical direction: \(\pm 2\) cm. In this paper, we have mainly focused on researching structural vertical vibration, and thus a proper denoising method is needed to ensure monitoring accuracy. Empirical mode decomposition (EMD) [13] is an efficient means of processing nonlinear and nonstationary signals, and decomposes signals into a number of monochromatic intrinsic mode functions (IMFs). After the EMD process, there may be mode mixing in IMFs. A novel method called ensemble empirical mode decomposition (EEMD) has been proposed to alleviate this problem [14]. At the core of the EEMD method is the addition of white noise. In this way, the mode mixing problem is alleviated. However, this creates new problems. The additional white noise is not able to be completely removed, and the interactions between signal and noise will bring different modes. To overcome these situations, the complementary ensemble empirical mode decomposition (CEEMD) method was developed [15]. By adding positive and negative white noise into the signal, the reconstruction error caused by the white noise can be reduced and the computational efficiency is increased. Nevertheless, this method cannot solve the problem of the additional mode caused by adding different noise signals. Thus, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was introduced [16]. This method realizes an accurate reconstruction of the decomposed signal and solves the additional mode problem. Additionally, a few novel EMD methods have been proposed in recent years (e.g., the step-by-step EMD method [17; 18] and the wavelet-bounded EMD method [19; 20]). Just as in EMD, the traditional wavelet is another efficient technique and has been widely applied in signal processing domain [21; 22; 23; 24; 25].