Level set estimation with dynamic sparse sensing

Jing Yang, Zuoen Wang, Jingxian Wu

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

5 Scopus citations

Abstract

In this paper, we study the level set estimation of a spatial-temporally correlated random field by using a small number of spatially distributed sensors. The level sets of a random field are defined as regions where data values exceed a certain threshold. We propose a new active sparse sensing and inference scheme, which can accurately extract level sets in a large random field with a small number of sensors strategically and sparsely placed in the random field. In the proposed active sparse sensing scheme, a central controller dynamically selects a small number of sensing locations according to the information revealed from past measurements, with the objective to minimize the expected level set estimation errors. The expected estimation error is explicitly expressed as a function of the sensing locations, and the results are used to formulate optimal and sub-optimal selection of sensing locations. Simulation results demonstrate that the proposed algorithms can achieve significant performance gains over baseline passive sensing algorithms that do not proactively select the sensing locations.

Original languageEnglish (US)
Title of host publication2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages487-491
Number of pages5
ISBN (Electronic)9781479970889
DOIs
StatePublished - Feb 5 2014
Event2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States
Duration: Dec 3 2014Dec 5 2014

Publication series

Name2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014

Other

Other2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
CountryUnited States
CityAtlanta
Period12/3/1412/5/14

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems

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