Analysis of Angle of Arrival Estimation Algorithms for Basal Ice Sheet Tomography

One of the key requirements for deriving more realistic ice sheet models is to obtain a good set of basal measurements that enable accurate estimation of bed roughness and conditions. For this purpose, 3D tomography of the ice bed has been successfully implemented with the help of angle of arrival estimation (AoA) algorithms such as multiple signal classification (MUSIC) and maximum likelihood estimation (MLE) techniques. These methods have enabled fine resolution in the cross-track dimension using synthetic aperture radar (SAR) images obtained from single pass multichannel data. This project analyzes and compares the results obtained from the spectral MUSIC algorithm, an alternating projection (AP) based MLE technique, and a relatively recent approach called the reiterative super resolution (RISR) algorithm. While the MUSIC algorithm is more attractive computationally compared to MLE, the performance of the latter is known to be superior in a low signal to noise ratio regime. The RISR algorithm takes a completely different approach by using a recursive implementation of the minimum mean square error (MMSE) estimation technique instead of using the sample covariance matrix (SCM) that is central to subspace based algorithms. This renders the algorithm more robust in scenarios where there is a very low sample support. The SAR focused datasets provide a good case study to explore the performance of the three techniques to the application of ice sheet bed elevation estimation.