Object detectability enhancement under weak signals for passive GNSS-based SAR

Abstract

Passive Global Navigation Satellite System (GNSS)-based Synthetic Aperture Radar (SAR), known as GNSS-SAR, is a recently developing SAR imaging system. Due to the restrictions of transmission power and long distance transmission between GNSS satellites and earth surface, the received signals can be very weak after reflections, in which a noisy GNSS-SAR image can be resulted in. In this study, a new imaging algorithm for GNSS-SAR objects signal detectability enhancement is proposed. The main idea of the proposed algorithm is to apply joint coherent and non-coherent integrations for azimuth compression processing for each scattering point. In the proposed algorithm, at first, each azimuth resolution cell is partitioned into multiple non-overlapped consecutive mini-slots. To both effectively average out the remaining noise from range compression and reduce azimuth samples for correlation operation, the azimuthally distributed range-compressed signals with migration corrected in each partitioned mini-slot are added together. Then azimuth correlation for the compression per azimuth cell is carried out based on the result obtained from performing the addition scheme. Both theoretical analysis and experimental study show that the proposed algorithm can result in obviously enhanced imaging signal detectability for object identification. Meanwhile, computation with the proposed imaging algorithm is significantly more efficient than with the conventional GNSS-SAR imaging algorithm.