Nonparametric change detection in 2D random sensor field

Ting He, Shai Ben-David, Lang Tong

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

4 Scopus citations


The problem of detecting changes from data collected from a large-scale randomly deployed two dimensional sensor field is considered. Under a nonparametric change detection framework, we propose detection algorithms using two measures of change. Theoretical performance guarantee is derived from the Vapnik-Chervonenkis theory. By exploiting the structures of the search domain, we design a suboptimal recursive algorithm to detect the area of largest change which, for M sample points, runs in time O(M2 log M) (compared to an O(M4) required for a straightforward exhaustive search). The lost of performance diminishes as M increases.

Original languageEnglish (US)
Title of host publication2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Signal Proces. Education, Spec. Sessions
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)0780388747, 9780780388741
StatePublished - 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149


Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Country/TerritoryUnited States
CityPhiladelphia, PA

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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