Explore MATLAB source code curated for "匹配追踪" with clean implementations, documentation, and examples.
The Matching Pursuit (MP) algorithm achieves signal sparse decomposition using projection-based methodology, iteratively selecting dictionary atoms that best match signal residuals through orthogonal projections.
A sparse decomposition signal reconstruction program utilizing matching pursuit methodology to iteratively match signal components and ultimately reconstruct the original signal through optimized atom selection and combination.
Sparse decomposition strictly adheres to matching pursuit principles to generate new atoms that iteratively match with the signal until only residual components remain, implementing an iterative optimization algorithm for signal approximation.
Comprehensive compressed sensing reconstruction algorithm source code, featuring matching pursuit series, norm minimization approaches, and various optimization techniques with implementation examples.
Implementation of Matching Pursuit and Orthogonal Matching Pursuit algorithms for time-frequency analysis with code-level optimizations and mathematical foundations.
Implementation of matching pursuit algorithm for sparse decomposition of radar signals to obtain their sparse representations
Sparse Reconstruction Challenges in Compressed Sensing with Algorithmic Enhancements