Custom Implementation of Compressive Sensing Using Matching Pursuit Algorithm

In this technical documentation, the author discusses compressive sensing and the matching pursuit algorithm through a custom-developed code implementation. The codebase includes Chinese annotations for clarity. To elaborate further on these concepts: compressive sensing is a signal processing technique that enables reconstruction of high-dimensional data from low-dimensional measurements by leveraging signal sparsity. The matching pursuit algorithm serves as a greedy iterative method for sparse approximation, commonly applied in computer vision for object tracking across video frames and in signal reconstruction tasks. The author's implementation encapsulates these techniques through modular functions, allowing users to directly invoke the core algorithm. Key components include: - Signal preprocessing and measurement matrix generation - Iterative residual calculation and atom selection from dictionaries - Convergence checks based on reconstruction error thresholds Future applications of this implementation span diverse domains such as wireless communication systems (for efficient data transmission) and video surveillance (for motion tracking and compressed video storage). The code structure emphasizes computational efficiency through optimized matrix operations and provides configurable parameters for sparsity levels and iteration limits.