Compressive sensing based high-resolution millimeter-wave SAR imaging at low SNR

This paper presents a novel Compressive Sensing (CS) algorithm for Synthetic Aperture Radar (SAR) imaging, designed to overcome the resolution limitations found in conventional methods, particularly in low Signal-to-Noise Ratio (SNR) scenarios. By employing Millimeter-wave SAR, the proposed approach offers a robust solution for precise object detection and localization challenges. The study models SAR imaging using Linear Frequency Modulated signals to meet the requirements of the CS framework. Performance evaluations are conducted across various noise levels to assess the effectiveness of the proposed Modified-Orthogonal Matching Pursuit (M-OMP) algorithm. This algorithm is compared with other established algorithms, including Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit (CoSaMP), and Adaptive CoSaMP (A-CoSaMP). Results indicate that the M-OMP algorithm outperforms the other algorithms presented in this paper and surpasses existing methods in the literature in accurately identifying and localizing targets, even in low SNR conditions as \(-\) $-$ 10 dB.