Sub-Nyquist SAR via Fourier Domain Range Doppler Processing

TL;DR

This paper introduces a novel Fourier domain processing algorithm for SAR that enables sub-Nyquist sampling in range and azimuth, utilizing compressed sensing to reduce data rates while maintaining high-resolution imaging.

Contribution

The paper presents a new Fourier-based Range-Doppler processing method that allows SAR data to be processed at sub-Nyquist rates using compressed sensing, enabling reduced sampling and adaptive spectral utilization.

Findings

Successful recovery of SAR images from limited bandwidth data.

Effective reduction of transmitted pulses while maintaining image quality.

Implementation demonstrated on hardware with real and simulated data.

Abstract

Conventional Synthetic Aperture Radar (SAR) systems are limited in their ability to satisfy the increasing requirement for improved spatial resolution and wider coverage. The demand for high resolution requires high sampling rates, while coverage is limited by the pulse repetition frequency. Consequently, sampling rate reduction is of high practical value in SAR imaging. In this paper, we introduce a new algorithm, equivalent to the well-known Range-Doppler method, to process SAR data using the Fourier series coefficients of the raw signals. We then demonstrate how to exploit the algorithm features to reduce sampling rate in both range and azimuth axes and process the signals at sub-Nyquist rates, by using compressed sensing (CS) tools. In particular, we demonstrate recovery of an image using only a portion of the received signal's bandwidth and also while dropping a large percentage of the transmitted pulses. The complementary pulses may be used to capture other scenes within the same coherent processing interval. In addition, we propose exploiting the ability to reconstruct the image from narrow bands in order to dynamically adapt the transmitted waveform energy to vacant spectral bands, paving the way to cognitive SAR. The proposed recovery algorithms form a new CS-SAR imaging method that can be applied to high-resolution SAR data acquired at sub-Nyquist rates in range and azimuth. The performance of our method is assessed using simulated and real data sets. Finally, our approach is implemented in hardware using a previously suggested Xampling radar prototype.