Opportunistic sensor activation in the face of data deluge

Kamil Nar, Sourabh Bhattacharya, Tamer Başar

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


In this paper, we consider the problem of designing optimal measurement policies for a sensor that acquires sequential compressive measurements of a static vector of unknown sparsity as originally formulated in [3]. The scenario is modeled as a finite horizon sequential decision making problem when the number of samples is strictly restricted to be less than the overall horizon of the problem. We assume that at each instant of time the sensor can decide whether or not to take an observation, based on the quality of the sensing parameters. The objective of the sensor is to minimize the coherence of the final sensing matrix. We provide a closed-loop optimal measurement policy for a low-dimensional problem. We generalize the optimal policy to obtain a feasible policy for acquiring arbitrary length sparse vectors of unknown sparsity. Finally, we illustrate the performance of the proposed policy by providing simulation results.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781467357173
StatePublished - 2013
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other52nd IEEE Conference on Decision and Control, CDC 2013

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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