Compressive sensing with unknown parameters

Marco Rossi, Alexander M. Haimovich, Yonina C. Eldar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


This work addresses target detection from a set of compressive sensing radar measurements corrupted by additive white Gaussian noise. In previous work, we studied target localization using compressive sensing in the spatial domain, i.e., the use of an undersampled MIMO radar array, and proposed the Multi-Branch Matching Pursuit (MBMP) algorithm, which requires knowledge of the number of targets. Generalizing the MBMP algorithm, we propose a framework for target detection, which has several important advantages over previous methods: (i) it is fully adaptive; (ii) it addresses the general multiple measurement vector (MMV) setting; (iii) it provides a finite data records analysis of false alarm and detection probabilities, which holds for any measurement matrix. Using numerical simulations, we show that the proposed algorithm is competitive with respect to state-of-the-art compressive sensing algorithms for target detection.

Original languageEnglish (US)
Title of host publicationConference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Number of pages5
StatePublished - 2012
Event46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States
Duration: Nov 4 2012Nov 7 2012

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Country/TerritoryUnited States
CityPacific Grove, CA

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications


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