## 2 Literature Review
### Classification of the Subspace System Identification Methods  
Recent researches greatly deals with application of ambient excitation for damage detection and modal analysis of the in-service structures. Output-only subspace system identification also referred as stochastic subspace identification (SSI), could be simply adapted to identify dynamic parameters in ambient excitation. In a pioneering work, [PERSON] et al. [32] introduced stochastic subspace identification together with combined and deterministic models within a unified framework. The proposed stochastic subspace method used Hankel block matrix of the output data to analyze system and to extract state space model. Due to the direct use of the response data in the identification process, the method is named data-driven stochastic subspace identification or SSI-DATA. The state sequence matrix is calculated before deriving state-space equation. SSI-DATA is a numerically robust algorithm that uses QR decomposition to project future data on the past subspace [54]. The methodology of SSI-DATA is provided in Figure 1.  
The introduced identification method by [PERSON] et al. [32] has received considerable attention due to its well-defined algorithm and data structure. However, the aforementioned algorithm is not suitable for complex data categories with a large number of sensors, large number of modes of interest, and existing turbulence or no-stationarity. To deal with the shortcomings of the algorithm proposed by [PERSON] et al. [32], several researchers proposed improving the convergence rates of transfer matrices to deal with large number of sensor data [55; 56; 57]. Studies such as those of [PERSON] and [PERSON] [33] or [PERSON] and [PERSON] [58] suggested to reduce the data complexity using subset data, so-called reference sensors. Advance processing of measurement data before the estimation of observability matrix [59; 60; 61] and introduction of recursive identification systems [62; 63; 64] are among the proposed solutions. In order to deal with complex data, [PERSON] and [PERSON] [65] introduced a new SSI-DATA algorithm using multi-order system identification. In this method a fast computation scheme using multiple-order observability matrix is suggested to solve the least squares problem. The computational burden of the proposed algorithm is much lower than the conventional algorithms. In another research, [PERSON] and [PERSON] [27] proposed an efficient SSI algorithm by reformulation and computation of uncertainty bounds. The obtained results from application of the method on Z24 Bridge showed that the algorithm is both computationally and memory efficient. Figure 1: Methodology of the data-driven stochastic subspace identification (SSI-DATA) technique. Nowadays, wireless sensor networks (WSNs) are widely used in SHM. However, the computational load is one of the main concerns regarding the application of WSNs. Hence, it is necessary to significantly reduce the computational burden and data processing efforts. Centralized algorithms are not suitable for sensor applications due to impractical computational and communication load, as well as its increased vulnerability. [PERSON] et al. [66] presented a decentralized SSI-DATA algorithm implemented on the Imote2-based WSNs. The results obtained from an experimental test of a five-story shear building shows a similar accuracy for the centralized and decentralized subspace system identification algorithms. Classical covariance-based subspace algorithms [67; 68; 69] took advantage of using output data to calculate covariance. To deal with output-only measurement, [PERSON] and [PERSON] [33] used covariance between outputs and a reference outputs for health monitoring of ambient excited civil structures. The proposed SSI-COV method used correlation functions for modal identification. In this method, the response signal of the applied ambient excitation is considered as Gaussian white noise, equal to the covariance of the response signal.