ARRAY PROCESSING TECHNIQUES FOR ESTIMATION AND TRACKING OF AN ICE-SHEET BOTTOM

• Main Author: Al-Ibadi, Mohanad
• Other Authors: Blunt, Shannon, Paden, John, Stiles, James, Perrins, Erik, Allen, Christopher, Fang, Huazhen
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: University of Kansas 2019
• Subjects: Electrical engineering; Direction of arrival estimation; Model order estimation; Radar imaging; Sequential tracking; Synthetic aperture radar; Arctic; Canadian Arctic Archipelago; Arctic Archipelago; Ice Sheet
• Online Access: http://hdl.handle.net/1808/29629; http://dissertations.umi.com/ku:16582

Ice bottom topography layers are an important boundary condition required to model the flow dynamics of an ice sheet. In this work, using low frequency multichannel radar data, we locate the ice bottom using two types of automatic trackers. First, we use the multiple signal classification (MUSIC) beamformer to determine the pseudo-spectrum of the targets at each range-bin. The result is passed into a sequential tree-reweighted message passing belief-propagation algorithm to track the bottom of the ice in the 3D image. This technique is successfully applied to process data collected over the Canadian Arctic Archipelago ice caps in 2014, and produce digital elevation models (DEMs) for 102 data frames. We perform crossover analysis to self-assess the generated DEMs, where flight paths cross over each other and two measurements are made at the same location. Also, the tracked results are compared before and after manual corrections. We found that there is a good match between the overlapping DEMs, where the mean error of the crossover DEMs is 38±7 m, which is small relative to the average ice-thickness, while the average absolute mean error of the automatically tracked ice-bottom, relative to the manually corrected ice-bottom, is 10 range-bins. Second, a direction of arrival (DOA)-based tracker is used to estimate the DOA of the backscatter signals sequentially from range bin to range bin using two methods: a sequential maximum a posterior probability (S-MAP) estimator and one based on the particle filter (PF). A dynamic flat earth transition model is used to model the flow of information between range bins. A simulation study is performed to evaluate the performance of these two DOA trackers. The results show that the PF-based tracker can handle low-quality data better than S-MAP, but, unlike S-MAP, it saturates quickly with increasing numbers of snapshots. Also, S-MAP is successfully applied to track the ice-bottom of several data frames collected from over Russell glacier in 2011, and the results are compared ...