Date of Award
2025
Degree Type
Thesis
Degree Name
Master of Science in Electrical Engineering (MSEE)
Department
Electrical, Computer, and Biomedical Engineering
First Advisor
Richard J. Vaccaro
Abstract
OSE (Optimal Subspace Estimation) is an algorithm that obtains an estimated subspace from structured data observed in noise. While OSE is able to obtain an accurate subspace estimate with a small number of snapshots, it has not been demonstrated how this affects the performance of many applications that leverage OSE. One such application is beamforming; Using an OSE-based beamformer, this thesis will apply common performance metrics to measure how much of an advantage an accurate subspace estimate provides. This algorithm will be compared against other widely used beamformers such as MPDR and DMR to benchmark its efficacy. Mismatch is then introduced to demonstrate how well an OSE-based beamformer may operate when perfect information about the sensor array is not available.
Also examined are a few extensions to the OSE algorithm. Namely, Nested OSE is used to obtain estimates of subspaces measured by nested arrays. A correction algorithm is also introduced to enforce an undamped constraint on the shift OSE enforces. This new algorithm can be compared against an even tighter Cramer-Rao bound on subspace distance, providing a more accurate subspace estimate than standard OSE is able to provide. Finally, an algorithm is suggested to obtain an estimate of rank using the OSE subspace estimate. This is a necessary input for the OSE-beamformer and many other ABF alternatives.
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Recommended Citation
Dunn, Brendan, "OPTIMAL SUBSPACE ESTIMATION FOR LINEAR NESTED ARRAYS: APPLICATIONS AND PERFORMANCE METRICS" (2025). Open Access Master's Theses. Paper 2684.
https://digitalcommons.uri.edu/theses/2684