###### Abstract  
This article presents a hybrid method of structural modal parameter identification, based on improved empirical mode decomposition (EMD) and autoregressive and moving average (ARMA). Special attention is given to some implementation issues, such as the modal mixing, false modes, the judgment of the real intrinsic mode function (IMF) of classical EMD, and the difficulty of fixing the order of ARMA. To resolve the existing defects of EMD, an improved EMD (IEMD) that combines frequency band filtering and cluster analysis is proposed in this paper, where frequency band filtering divides the signal into several narrowband signals before the EMD process, and cluster analysis is used to determine the real IMFs. Euclidean distance is used to cluster the decomposition results, with no need to adjust any indexes or thresholds, and only by means of using the nearest distance to efficiently determine the real IMF. Moreover, IEMD is used as a pre-processing tool for ARMA, to resolve the difficulty of fixing its order. The capabilities of the proposed method were compared and assessed using a numerical simulation and an experimental model. The numerical simulation and experimental results showed that the improved method could resolve the modal mixing and false modal problems in the classical EMD process and could automatically identified the real IMFs, while the proposed IEMD was combined with ARMA to successfully identify the frequency and mode shape of the structure. Additionally, since each IMF is a single component signal, it is easy to determine the order of the ARMA model.  
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method. After HHT was proposed, it was widely applied in many fields of engineering. In the actual application of HHT, the scholars found that the HHT method for structural modal parameter identification often encounters problems, such as mode mixing, false mode, and endpoint effects; therefore, many scholars are trying to improve the classic EMD [18; 19]. At present, scholars have proposed relevant improvement methods based on independent component analysis (ICA) [20], singular value decomposition (SVD) [21], and empirical mode decomposition (EEMD) [22]. Among them, the most widely-accepted improvement is the EEMD proposed by [PERSON]. Unfortunately, the problems of mode mixing and endpoint effects in EMD have not been fundamentally solved. Many scholars are committed to solving the problem of the endpoint effect of EMD, but the results are often not satisfactory, and many methods have problems of poor adaptivity, a large amount of computation, and long operation time. On the other hand, mode mixing and false modal are other bottleneck problems that hinder the application of EMD. [PERSON] proposed the method of interrupt detection, to solve the phenomenon of mode mixing; namely, the results are directly observed and re-decomposed if modal mixing occurs. This method required human posterior judgment, and the decomposition efficiency was greatly reduced. Therefore, it is still necessary to efficiently solve the mode mixing of EMD. Inspired by recent auto-ID (automatic identification) technology [23; 24; 25], this paper tries to use cluster analysis to automatically identify the real IMFs.  
In summary, in order to improve the above mentioned shortcomings of ARMA and EMD and to also further expand the application of these methods, this paper proposes a new hybrid automatic method for structural modal parameter identification based on IEMD and ARMA, and the effect of the proposed method was verified using a seven-story steel frame test apparatus in the laboratory. This paper is organized as follows: in Section 2, the algorithms of ARMA and EMD are introduced. The improved ARMA based on IEMD is discussed in detail in Section 3. A numerical simulation and a seven-story steel test frame in the laboratory to verify its effects are discussed in Sections 4 and 5. The conclusions are given in the last part.