Explore MATLAB source code curated for "子空间" with clean implementations, documentation, and examples.
Perform singular value decomposition on the covariance matrix of arbitrary array output data to obtain eigenvalues and eigenvectors, then construct a spatial spectrum function utilizing the orthogonality between signal subspace and noise subspace with MATLAB implementation
Implementation of subspace-based blind channel estimation for MIMO-OFDM systems with ready-to-execute code examples
Subspace-based beamforming algorithms primarily leverage signal subspace projection theory and eigen-decomposition techniques for directional signal enhancement
MATLAB simulation routine for subspace-based blind multiuser detection with algorithm implementation and performance analysis
Object tracking algorithm leveraging self-learning subspace with particle filtering, featuring high-speed performance and robustness to noise through probabilistic state estimation.
This MATLAB program demonstrates the implementation of subspace identification algorithms for blind spot estimation, providing a practical framework for studying subspace-based system identification techniques with comprehensive computational examples and dataset analysis.
Linear Discriminant Analysis (LDA) is a dimensionality reduction technique that projects data into a subspace optimized for class separability. The algorithm identifies basis vectors that maximize inter-class variance while minimizing intra-class variance within the projected subspace. Implementation typically involves computing scatter matrices and solving a generalized eigenvalue problem to determine the optimal projection axes.