Submodular Sparse Sensing for Gaussian Detection with Correlated Observations

Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)


Detection of a signal under noise is a classical signal processing problem. When monitoring spatial phenomena under a fixed budget, i.e., either physical, economical or computational constraints, the selection of a subset of available sensors, referred to as sparse sensing, that meets both the budget and performance requirements is highly desirable. Unfortunately, the subset selection problem for detection under dependent observations is combinatorial in nature and suboptimal subset selection algorithms must be employed. In this work, different from the widely used convex relaxation of the problem, we leverage submodularity, the diminishing returns property, to provide practical near-optimal algorithms suitable for large-scale subset selection. This is achieved by means of low-complexity greedy algorithms, which incur a reduced computational complexity compared to their convex counterparts.

Original languageEnglish
Pages (from-to)4025-4039
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number15
Publication statusPublished - 2018


  • Greedy selection
  • sensor placement
  • sensor selection
  • sparse sensing
  • submodular optimization

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