Signal Processing Applications of Compressed Sensing
- Login to Download
- 1 Credits
Resource Overview
Detailed Documentation
In this paper, we discuss the applications of compressed sensing in signal processing. For this context, we employ the Orthogonal Matching Pursuit (OMP) algorithm for signal recovery and reconstruction. It is worth noting that OMP is a highly efficient and widely adopted algorithm that effectively recovers compressed signals. The fundamental principle of this algorithm involves an iterative process where the signal is matched against a signal dictionary (typically a measurement matrix), and reconstruction is performed based on matching results. The implementation typically involves iteratively selecting the dictionary atom with maximum correlation to the current residual, solving a least-squares problem to update the signal estimate, and computing new residuals until convergence criteria are met. Therefore, we can utilize the Orthogonal Matching Pursuit algorithm in signal recovery and reconstruction processes to achieve more efficient and accurate results.
- Login to Download
- 1 Credits