Detection of Abrupt Changes in Autonomous System Fault Analysis Using Spatial Adaptive Estimation of Nonparametric Regression

Alexander Kalmuk, Oleg Granichin, Olga Granichina, Mingyue Ding

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 Scopus citations

Abstract

The paper deals with the detection of abrupt changes in autonomous systems. We consider this problem in the presence of Gaussian noise and solve it in two steps. At first, spatial adaptive estimation of nonparametric regression is used to estimate the observable data. Then Filtered Derivative Algorithm is used to detect abrupt changes in the obtained data using an adaptive threshold. The estimation of this adaptive threshold is presented. This approach is then applied to demonstrate the slowdown detection of a small autonomous vehicle.

Original languageEnglish
Title of host publication2016 AMERICAN CONTROL CONFERENCE (ACC)
PublisherIEEE Canada
Pages6839-6844
Number of pages6
StatePublished - 2016
Event2016 American Control Conference (ACC) - Boston, United States
Duration: 6 Jul 20168 Jul 2016

Publication series

NameProceedings of the American Control Conference
PublisherIEEE
ISSN (Print)0743-1619

Conference

Conference2016 American Control Conference (ACC)
CountryUnited States
CityBoston
Period6/07/168/07/16

Keywords

  • THRESHOLD

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