MATLAB Implementation of Stochastic Subspace Method

This article presents the main MATLAB program code for stochastic subspace method implementation, which enables high-precision signal identification. The code is developed following the methodologies described in "Dielectric Loss Angle Measurement Method Using Stochastic Subspace and Least Squares" and Dr. Chang Jun's dissertation "Application Research of Stochastic Subspace Method in Bridge Modal Parameter Identification". The implementation incorporates key algorithms including Hankel matrix construction, singular value decomposition (SVD) for system order determination, and state-space model estimation. The code provides researchers with robust tools for accurate data analysis in dielectric loss angle measurement and bridge modal parameter identification. It features automated parameter estimation routines and includes validation modules for result verification. The implementation can be easily extended to other signal identification and data analysis domains due to its modular structure and reliable handling of complex datasets. Key functions include data preprocessing, covariance computation, and modal parameter extraction algorithms that ensure numerical stability and accuracy.