2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSS-14.5
Paper Title Online Antenna Selection for Enhanced DOA Estimation
Authors Elias Aboutanios, University of New South Wales, Australia; Hamed Nosrati, CSIRO Australia, Australia; Xiangrong Wang, Beihang University, China
SessionSS-14: Robust Sensing and Detection in Congested Spectrum
LocationGather.Town
Session Time:Friday, 11 June, 11:30 - 12:15
Presentation Time:Friday, 11 June, 11:30 - 12:15
Presentation Poster
Topic Special Sessions: Robust Sensing and Detection in Congested Spectrum
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract The performance of direction of arrival (DOA)estimation using antenna arrays is fundamentally limited by the Cramer-Rao bound (CRB), which is intimately tied to the array configuration. In systems where a subset of antennas can be selected from a larger array, the array configuration can be recruited to enhance the DOA estimation performance by choosing the optimal subarray that minimizes the CRB. This strategy has been demonstrated to provide performance improvements, albeit at a substantial computational cost. Here, we leverage the power of unsupervised learning to reduce the computational cost of antenna selection for enhanced DOA estimation resulting in a practical online implementation of the array reconfiguration. We formulate the changing DOA estimation problem as a game in the context of online convex optimization, and employ a gradient-based technique that makes a move at each step in order to minimize the total loss after T steps. We compare the performance of the proposed method with the exhaustive search and a Dinkelbach-type algorithm that provides an approximate solution. We show that the proposed method is able to provide a solution that is close to the exhaustive search at a fraction of the computational time, thus permitting online implementation of the selection strategy.