Insense: Incoherent sensor selection for sparse signals

Amirali Aghazadeh, Mohammad Golbabaee, Andrew Lan, Richard Baraniuk

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal. In this paper, we develop a new sensor selection algorithm for sparse (or near sparse) signals that finds a subset of sensors that best recovers such signals from a number of measurements that is much smaller than the dimension of the signal. Existing sensor selection algorithms cannot be applied in such situations. Our proposed Incoherent Sensor Selection (Insense) algorithm minimizes a coherence-based cost function that is adapted from recent results in sparse recovery theory. Using six datasets, including two real-world datasets on microbial diagnostics and structural health monitoring, we demonstrate the superior performance of Insense for sparse-signal sensor selection.

Original languageEnglish (US)
Pages (from-to)57-65
Number of pages9
JournalSignal Processing
StatePublished - Sep 2018


  • Coherence
  • Compressive sensing
  • Optimization
  • Sensor selection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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