MATLAB Implementation of PASTd Algorithm for Subspace Tracking

This MATLAB program implements the PASTd (Projection Approximation Subspace Tracking with deflation) algorithm, a classical approach for dynamic subspace tracking. The algorithm operates by processing input data matrices through recursive updating mechanisms, effectively extracting and tracking subspace components in real-time applications. Key implementation features include: - Efficient eigenvalue decomposition updates using deflation techniques - Sequential processing of data vectors through projection approximation - Automated subspace dimension adaptation based on signal energy thresholds The MATLAB implementation utilizes matrix operations and orthogonalization procedures to maintain numerical stability, with core functions handling covariance approximation and basis vector updates. Through proper configuration of forgetting factors and convergence thresholds, PASTd demonstrates reliable performance in applications like signal processing, array beamforming, and pattern recognition. This implementation provides researchers with practical insights into subspace tracking methodologies, enabling improved results in fields requiring dynamic signal analysis and feature extraction. The code structure allows for straightforward integration with existing MATLAB workflows while maintaining computational efficiency for large-scale data processing.