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PUBLICATIONS

Optimized Sparse Sampling Lattices

Published

Author(s)

Peter Vouras, Mohamed Hany, Sudantha Perera, Carnot Nogueira, Rick Candell, Kate Remley

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

Download Paper

https://doi.org/10.1109/IEEECONF56349.2022.10051885
Local Download
Wireless (RF) and Advanced communications

Citation

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 22, 2025)

Issues

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Created March 7, 2023, Updated March 23, 2023
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