# Review of Structural Modal Tracking in Operational Modal Analysis: Methods and Applications
## 1 Introduction  
As fundamental characterizations of structural dynamic behavior, accurate modal parameters provide crucial information for structural condition assessment [1, 2], model updating [3], and damage detection [4, 5, 6]. Furthermore, with the swift advancement of data acquisition technologies and significant breakthroughs in system identification methodologies, structural health monitoring (SHM) based on automatic operational modal analysis (AOMA) has garnered extensive attention in various civil engineering projects, such as high-rise buildings [7, 8], long-span bridges [9, 10, 11, 12], dams [13], and churches [14, 15]. Within a continuous monitoring program, the modal parameters estimated from each dataset must be compared and linked with those obtained in previous responses [16]. Consequently, a complete AOMA framework should contain two core components [17]: (i) automatic modal parameter estimation (AMPE) for extracting valid modes from individual monitoring data segments and (ii) robust modal tracking mechanisms to capture the evolution of structural modal parameters through repeated AMPE. Reliable AMPE constitutes the fundamental prerequisite for modal tracking, as excessive spurious modes might disrupt the comparison and linking of modal parameters acrossdifferent time segments. The inherent limitations of first-generation modal estimation methods, particularly their reliance on manual interpretation for physical mode extraction, have driven significant progress in AMPE techniques. Among them, frequency domain methods predominantly automate peak detection in power spectral density (PSD) function [18], such as the automated frequency domain decomposition (FDD) method [19; 20], the enhanced least-squares complex frequency domain method [21], and the poly-reference least-squares complex frequency domain method [22]. Nevertheless, these methods encounter restricted practical adoption due to challenges including sensitivity to noise and reliance on static thresholds [23]. In contrast, the time domain method uses system identification techniques, such as stochastic subspace identification (SSI) [24] and the eigensystem realization algorithm (ERA) [25; 26], as basic tools to automatically interpret stabilization diagrams [27; 28; 29]. Stabilization diagrams, which graphically correlate modal frequencies with system orders, effectively mitigate the model order uncertainty inherent in the identification process. For example, [PERSON] et al. [16] implemented automated interpretation of a stabilization diagram with hierarchical clustering. [PERSON] [30] employed modal phase collinearity (MPC) to eliminate spurious modes and obtain representative physical mode clusters. [PERSON] et al. introduced fast density- and grid-based (DGB) clustering for automatic extraction of physical modes [31]. While AMPE has achieved remarkable progress, the long-term evolution of modal parameters offers deeper insights into structural dynamic characteristics, underscoring the growing imperative for modal tracking in AOMA. Owing to alterations in the structural dynamic characteristics [32; 33] and signal-to-noise ratios of response signals caused by ambient perturbations, such as temperature fluctuations [34; 35] and wind load variations [36], continuous AMPE cannot consistently guarantee the exclusion of spurious modes or the detection of all physical modes during long-term monitoring. Consequently, robust modal tracking methods must be implemented to aggregate reliable modal parameters across heterogeneous environmental conditions. Modal tracking represents an emerging paradigm that differs from modal estimation. Traditional implementations typically involve two sequential operations: (i) establishing reference modes (also referred to as baseline lists in certain studies [37]) based on estimated frequencies and mode shapes, and (ii) comparing the modes obtained from the currently recorded datasets with previously established reference modes and linking them in order.