Vouras, P.
, Hany, M.
, Perera, S.
, Nogueira, C.
, Candell, R.
and Remley, K.
(2023),
Optimized Sparse Sampling Lattices, Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, US, [online], https://doi.org/10.1109/IEEECONF56349.2022.10051885, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935319
(Accessed October 31, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Optimized Sparse Sampling Lattices
Published
Author(s)
Peter Vouras, , , , ,
Abstract
Sparse sampling approaches have been widely studied to achieve less complex measurement systems while maintaining detection performance. In this paper, we derive a new gradient implementation of an alternating projections algorithm that determines the optimal locations for spatial samples in a sparse array lattice. In a second phase of the sparse array design, an adaptive beamformer is used to further reduce the overall sidelobe level. Simulated results show a significant reduction in grating lobes. The approach described herein is useful in wideband synthetic aperture channel sounding applications where reducing the spatial sample set has the potential to significantly reduce data acquisition time.Proceedings Title
Asilomar Conference on Signals, Systems, and Computers
Conference Dates
October 30-December 2, 2022
Conference Location
Pacific Grove, CA, US
Pub Type
Conferences
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created March 7, 2023, Updated March 23, 2023