Synthetic aperture radar (SAR) is a well proven radar imaging technology that enables the production of high-resolution images of targets and terrain. SAR can be operated at night and in adverse weather conditions, overcoming limitations of optical and infrared systems. Conventional SAR systems are limited in their ability to fulfill the increasing demand for improved spatial resolution and wider coverage. While the former demand requires high sampling rates which are difficult to attain in practice, the latter is upper bounded due to the pulse repetition frequency (PRF) which is dictated by the Nyquist theorem. Consequently, sampling rate reduction is of high practical value in radar imaging in both main dimensions of the system – range and azimuth. In this talk, 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. Using the framework of compressed sensing (CS), we then demonstrate how to exploit the new algorithm features to reduce sampling rate in both range and azimuth axes and process the signals effectively at sub-Nyquist rates. We show that an image can be reconstructed after dropping a large percentage of the transmitted pulses, with no resolution degradation and offer to exploit the complementary pulses to capture other scenes within the same coherent processing interval (CPI) using electronic beam steering. The proposed recovery algorithms exploit the inherent two-dimensional structure of the problem and form a new CS-SAR imaging method that can be applied to high-quality and high-resolution real SAR imaging data acquired at sub-Nyquist rates in both axes. The performance of the new algorithms will be assessed using simulated and real data sets.
October, 05, 2016
Dept. of Electrical Engineering Technion, Auditorium 1061