Compressed sensing arrays for frequency-sparse signal detection and geolocation

Benjamin A. Miller, Joel Goodman, Keith Forsythe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Compressed sensing (CS) can be used to monitor very wide bands when the received signals are sparse in some basis. We have developed a compressed sensing receiver architecture with the ability to detect, demodulate, and geolocate signals that are sparse in frequency. In this paper, we evaluate detection, reconstruction, and angle of arrival (AoA) estimation via Monte Carlo simulation and find that, using a linear 4-sensor array and undersampling by a factor of 8, we achieve near-perfect detection when the received signals occupy up to 5% of the bandwidth being monitored and have an SNR of 20 dB or higher. The signals in our band of interest include frequency-hopping signals detected due to consistent AoA. We compare CS array performance using sensor-frequency and space-frequency bases, and determine that using the sensor-frequency basis is more practical for monitoring wide bands. Though it requires that the received signals be sparse in frequency, the sensor - frequency basis still provides spatial information and is not affected by correlation between uncompressed basis vectors.

Original languageEnglish
Title of host publicationDepartment of Defense Proceedings of the High Performance Computing Modernization Program - Users Group Conference, HPCMP-UGC 2009
Pages297-301
Number of pages5
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 DoD High Performance Computing Modernization Program - Users Group Conference, HPCMP-UGC 2009 - San Diego, CA, United States
Duration: 15 Jun 200918 Jun 2009

Publication series

NameDepartment of Defense Proceedings of the High Performance Computing Modernization Program - Users Group Conference, HPCMP-UGC 2009

Conference

Conference2009 DoD High Performance Computing Modernization Program - Users Group Conference, HPCMP-UGC 2009
Country/TerritoryUnited States
CitySan Diego, CA
Period15/06/0918/06/09

Keywords

  • Block-sparse reconstruction
  • Compressive sensing
  • Sensor arrays
  • Space-frequency sparse reconstruction

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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