Application of Subspace Methods in Blind Spot Estimation

This program is specifically developed for applying subspace methods to blind spot estimation, primarily aimed at enhancing the efficiency and accuracy of learning subspace identification algorithms. The implementation leverages MATLAB's matrix computation capabilities and system identification toolbox functions to handle high-dimensional data processing. Key algorithmic components include orthogonal projections, singular value decomposition (SVD) for subspace separation, and statistical estimation techniques for blind spot detection. Prior familiarity with MATLAB development environment is recommended before using this program. Beyond core computational functionalities, the package includes detailed usage documentation with step-by-step code explanations, along with sample datasets containing multi-sensor measurements and validation scenarios. The code structure features modular design with separate functions for data preprocessing, subspace decomposition, parameter estimation, and result visualization using MATLAB's plotting capabilities. We hope this program serves as a valuable resource for students and researchers studying subspace identification algorithms, providing both theoretical foundations and practical implementation insights through executable MATLAB code and comparative analysis examples.