Near-Optimal Greedy Sensor Selection for MVDR Beamforming with Modular Budget Constraint

Mario Coutino, Sundeep Chepuri, Geert Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)


In this paper, we present a greedy sensor selection algorithm for minimum variance distortionless response (MVDR) beamforming under a modular budget constraint. In particular, we propose a submodular set-function that can be maximized using a linear-time greedy heuristic that is near optimal. Different from the convex formulation that is typically used to solve the sensor selection problem, the method in this paper neither involves computationally intensive semidefinite programs nor convex relaxation of the Boolean variables. While numerical experiments show a comparable performance between the convex and submodular relaxations, in terms of output signal-to-noise ratio, the latter finds a near-optimal solution with a significantly reduced computational complexity.
Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)978-0-9928626-7-1
Publication statusPublished - 2017
EventEUSIPCO 2017: 25th European Signal Processing Conference - Kos Island, Greece
Duration: 28 Aug 20172 Sep 2017
Conference number: 25


ConferenceEUSIPCO 2017
Abbreviated titleEUSIPCO
CityKos Island
Internet address


  • submodularity
  • MVDR beamforming
  • greedy algorithm
  • budget constraint
  • sensor selection

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