Validation of CReSIS Synthetic Aperture Radar Processor and Optimal Processing Parameters

Abstract

Sounding the ice sheets of Greenland and Antarctica is a vital component in determining the effect of global warming on sea level rise. Of particular importance are measurements of the bedrock topography of the outlet glaciers that transport ice from the ice sheet's interior to the margin where it calves into icebergs, contributing to sea level rise. These outlet glaciers are difficult to sound due to crevassing caused by the relatively fast movement of the ice in the glacial channel and higher signal attenuation caused by warmer ice. The Center for Remote Sensing of Ice Sheets (CReSIS) uses multi-channel airborne radars which employ methods for achieving better resolution and signal-to-noise ratio (SNR) to better sound outlet glaciers. Synthetic aperture radar (SAR) techniques are used in the along-track dimension, pulse compression in the range dimension, and an antenna array in the cross-track dimension. CReSIS has developed the CReSIS SAR processor (CSARP) to effectively and efficiently process the data collected by these radars in each dimension. To validate the performance of this processor a SAR simulator was developed with the functionality to test the implementation of the processing algorithms in CSARP. In addition to the implementation of this simulator for validation of processing the data in the along-track, cross-track and range dimensions, there are a number of data-dependent processing steps that can affect the quality of the final data product. CSARP was tested with an ideal simulated point target in white Gaussian noise. The SNR change achieved by range compression, azimuth compression, array combination with and without matched filtering, and lever arm application were all within .2 dB of the theoretical expectation. Channel equalization, when paired with noise-based matched filtering, provided 1-2 dB of gain on average but significantly less than the expected gain. Extending the SAR aperture length to sound bedrock will improve the along-track resolution, but at the expense of SNR. Increasing the taper of a window in the fast-time and slow-time will slightly improve the SNR of the data. Changing the relative permittivity used to process the data improved the resulting SNR by no more than 0.025 dB for the test dataset.