# Review of Structural Modal Tracking in Operational Modal Analysis: Methods and Applications
## 5 Discussion and Recommendations  
### Automatic Modal Estimation and Modal Tracking Method  
AMPE and modal tracking constitute the complete workflow of AOMA. AMPE aims to automatically extract modal parameters from individual datasets, eliminating human intervention. Research on AMPE has reached a relatively mature stage, primarily concentrating on automatic interpretation of stabilization diagrams through diverse clustering algorithms. Although these algorithms vary in their theoretical foundations, they share the same core, i.e., precise estimation of mode similarity between the poles in stabilization diagrams and rational threshold determination. Consequently, novel AMPE methods will still emerge in the future, and a pivotal challenge lies in achieving adaptive clustering thresholds to fulfill the requirements of engineering applications with diverse structures and data quality.  
Due to the complexity of operational conditions, the external excitation levels of structures vary continuously over time, leading to variations in the numbers and characteristics of identifiable modes at different times. Consequently, relying solely on AMPE methods makes it challenging to ensure continuous tracking of physical modes. Modal tracking addresses this issue by establishing dynamic correspondence between current estimation results and historical references, thereby linking abundant continuous modal parameters in order on a temporal scale to obtain long-term modal evolution. The modal tracking method has remained a research hotspot in the field of SHM over the past decade or so. Nonetheless, several limitations have emerged when traditional methods are implemented in practice and require improvement:  
* The determination of reference modes remains plagued by threshold sensitivity in clustering-based methods, making it challenging to achieve full automation. If modes in some orders are omitted from the reference mode list, their contributions will be disregarded in continuous modal tracking, resulting in the mode-missing phenomenon. Additionally, determining reference modes in situations with closely spaced modes presents another challenge.
* During the modal linking stage, it is necessary to define thresholds or similarity metrics to assess whether the modes estimated at different times share the same physical characteristics, which is difficult for non-experts. Specifically, small and large tolerances are suitable for tracking modes from monitoring data with high and low signal-to-noise ratios, respectively. Furthermore, changing environmental and operational conditions affect the dynamic behavior of structures, which may lead to a loss of critical information and erroneous linking, disrupting the modal tracking process.
* The criteria used to describe modal similarity must be determined carefully. Generally, the MAC, which is widely adopted in most studies, is appropriate when numerous sensors are installed at key locations on a structure but not in cases where only a few sensors are utilized.  
In summary, the complexity of operational conditions makes it difficult for initial tracking methods to ensure accurate matching and effective tracking of modal parameters. To address this, it is essential not only to investigate automated strategies for determining and updating reference modes but also to propose adaptive modal matching criteria, thereby establishing fully automatic modal tracking methods for real-time online monitoring. Furthermore, reference modes determined based on multiple response sets exhibit greater robustness against spurious modes and noise but may introduce delays or even fail when tracking rapidly changing modes. In contrast, reference modes derived from the most recent response set can track sudden modal changes but are more susceptible tospurious modes. Therefore, balancing the advantages of these two tracking strategies could significantly alleviate the current limitations in modal tracking.