GNSS-based SAR Imaging for Object Detection based on Iterative Range Compressions

In recent decades, passive global navigation satellite system-based synthetic aperture radar (GNSS–SAR) has emerged as a remote sensing tool. However, the distance between GNSS satellites and the earth’s surface results in weak signal strength, which negatively affects object detection. To address this issue, this paper proposes an imaging method based on an iterative range compression scheme. In the proposed method, range compression is first performed through the correlation of the reflected signal with the local direct signal replica. Meanwhile, an iterative rangematched filter is generated through the auto-correlation of the local replica. Thereafter, the first-round iteration is conducted through the correlation of the initial range-compressed pulse with the iterative range-matched filter. A noise threshold is established. If the noise of the compressed signal in the first iteration round is higher than the threshold, the signal is correlated with the iterative range-matched filter, initiating another iteration round. Otherwise, the system proceeds to the next step, that is, azimuth compression. To examine the proposed imaging method, simulation tests and field experimental validation are conducted using the BeiDou B3I signal. The results demonstrate that the proposed imaging method can provide a significantly higher image contrast-to-noise ratio than a previously proposed state-of-the-art imaging method for weak signals in a bi-static GNSS-SAR system. Overall, the results demonstrate the ability of the proposed method to provide higher object detectability.