Performance Evaluation of Ramanujan Transform with OMP and BP Algorithms

In this study, we explore the application of compressed sensing techniques to Synthetic Aperture Radar (SAR) signal reconstruction using the Ramanujan Transform. SAR signal processing is critical for various remote sensing applications, necessitating efficient and accurate reconstruction methods. We implement two reconstruction algorithms: Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP), both leveraging the sparse representation capabilities of the Ramanujan Transform. The SAR signals are initially transformed using the Ramanujan Transform to obtain a sparse representation, which is then processed using OMP and BP for signal reconstruction. We evaluate the performance of both methods by comparing their Mean Squared Error (MSE) in reconstructing the original SAR signals. Our results demonstrate that while both methods are effective, there are notable differences in their reconstruction accuracy and computational efficiency. This comparative analysis provides valuable insights into the suitability of OMP and BP for SAR signal reconstruction, highlighting the potential of the Ramanujan Transform in enhancing compressed sensing techniques for remote sensing applications.