Discrete State Estimation with Persistent Sensor Faults and Non-Persistent Noise via Noisy Bayesian Active Diagnosis

Sze Yong, Necmiye Ozay

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

Abstract

In this paper, we consider adaptive decision-making problems for stochastic discrete state estimation with a given budget of sensing actions/measurements that yield noisy and/or faulty partial observations. This problem is an extension of Bayesian active diagnosis, which is known to be NP-hard, to the setting when the sensor measurements are vector-valued and may be affected by persistent sensor faults and/or non-persistent noise. In particular, we identify meaningful reward functions for this problem that are adaptive monotone and weakly adaptive submodular; thus an adaptive greedy algorithm (with no need for proxy reward functions nor new algorithms) has guaranteed near-optimal performance. Finally, we apply our approach to discrete state estimation via active sensing of an electrical power system with sensor faults (persistent noise) and sensor noise (stochastic/non-persistent noise).

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages313-320
Number of pages8
Volume2018-June
ISBN (Print)9781538654286
DOIs
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Other

Other2018 Annual American Control Conference, ACC 2018
CountryUnited States
CityMilwauke
Period6/27/186/29/18

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Yong, S., & Ozay, N. (2018). Discrete State Estimation with Persistent Sensor Faults and Non-Persistent Noise via Noisy Bayesian Active Diagnosis. In 2018 Annual American Control Conference, ACC 2018 (Vol. 2018-June, pp. 313-320). [8431639] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2018.8431639