MATLAB Code Implementation for Compressed Sensing Simulation

In this article, we will explore in detail a highly important topic: the simulation program for compressed sensing. This program implements the Orthogonal Matching Pursuit (OMP) algorithm, an efficient greedy algorithm that reconstructs one-dimensional input signals from under-sampled measurements. The implementation typically involves generating sparse signals, creating random measurement matrices, and iteratively selecting the most correlated atoms from the dictionary to approximate the original signal. Compressed sensing technology has gained widespread application in recent years, enabling better understanding and processing of signals through sparse representations. Compared to traditional signal processing methods, compressed sensing offers higher efficiency and superior performance by leveraging signal sparsity and random sampling. The simulation code generally includes key functions for: - Signal generation with controlled sparsity - Measurement matrix creation (typically random Gaussian or Bernoulli matrices) - OMP algorithm implementation with iteration control - Reconstruction error calculation and performance evaluation Through this simulation program, we can better understand and master this technology, subsequently applying it to practical engineering applications such as image compression, medical imaging, and wireless communications.