Research on Pulse Radar Imaging with Compressive Sensing Implementation

Pulse radar imaging has demonstrated significant value in recent years for applications such as target recognition and environmental monitoring. Traditional radar signal processing methods rely on the Nyquist sampling theorem, while compressive sensing theory breaks this limitation by enabling high-quality reconstruction of sparse signals from a small number of measurements.

The Orthogonal Matching Pursuit (OMP) algorithm serves as a key implementation approach in compressive sensing. Its core mechanism involves iteratively selecting atoms that exhibit maximum correlation with the residual signal to approximate the original signal. In radar imaging applications, the algorithm first constructs an overcomplete dictionary representing target echo characteristics. Through iterative atom selection and residual updates (typically implemented via pseudoinverse operations), it ultimately generates a two-dimensional sparse representation of the scene. Code implementation generally involves initializing residual_r = measurement_vector, then iteratively solving argmax|dictionary_matrix'*residual_r| to identify optimal atoms.

Compared to conventional methods like matched filtering, this sparsity-based approach significantly reduces data acquisition requirements while effectively suppressing noise interference. Notably, dictionary design must incorporate actual radar wavelength and observation angles to ensure accurate capture of target scattering characteristics. Experimental results demonstrate over 30% resolution improvement for imaging typical man-made targets such as vehicles and buildings.

Future research directions include integrating deep learning to optimize atom selection strategies and developing real-time OMP variants for dynamic scenarios. These advancements will further enhance pulse radar practicality in autonomous driving and remote sensing monitoring systems.