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Continuous estimation using context-dependent discrete measurements

Publication ,  Journal Article
Ivanov, R; Atanasov, N; Pajic, M; Weimer, J; Pappas, GJ; Lee, I
Published in: IEEE Transactions on Automatic Control
January 1, 2019

This paper considers the problem of continuous state estimation from discrete context-based measurements. Context measurements provide binary information as obtained from the system's environment, e.g., a medical alarm indicating that a vital sign is above a certain threshold. Since they provide state information, these measurements can be used for estimation purposes, similar to standard continuous measurements, especially when standard sensors are biased or attacked. Context measurements are assumed to have a known probability of occurring given the state; in particular, we focus on the probit function to model threshold-based measurements, such as the medical-alarm scenario. We develop a recursive context-aware filter by approximating the posterior distribution with a Gaussian distribution with the same first two moments as the true posterior. We show that the filter's expected uncertainty is bounded when the probability of receiving context measurements is lower bounded by some positive number for all system states. Furthermore, we provide an observability-like result - all eigenvalues of the filter's covariance matrix converge to 0 after repeated updates if and only if a persistence of excitation condition holds for the context measurements. Finally, in addition to simulation evaluations, we applied the filter to the problem of estimating a patient's blood oxygen content during surgery using real-patient data.

Duke Scholars

Published In

IEEE Transactions on Automatic Control

DOI

EISSN

1558-2523

ISSN

0018-9286

Publication Date

January 1, 2019

Volume

64

Issue

1

Start / End Page

238 / 253

Related Subject Headings

  • Industrial Engineering & Automation
  • 4007 Control engineering, mechatronics and robotics
  • 0913 Mechanical Engineering
  • 0906 Electrical and Electronic Engineering
  • 0102 Applied Mathematics
 

Citation

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Ivanov, R., Atanasov, N., Pajic, M., Weimer, J., Pappas, G. J., & Lee, I. (2019). Continuous estimation using context-dependent discrete measurements. IEEE Transactions on Automatic Control, 64(1), 238–253. https://doi.org/10.1109/TAC.2018.2797839
Ivanov, R., N. Atanasov, M. Pajic, J. Weimer, G. J. Pappas, and I. Lee. “Continuous estimation using context-dependent discrete measurements.” IEEE Transactions on Automatic Control 64, no. 1 (January 1, 2019): 238–53. https://doi.org/10.1109/TAC.2018.2797839.
Ivanov R, Atanasov N, Pajic M, Weimer J, Pappas GJ, Lee I. Continuous estimation using context-dependent discrete measurements. IEEE Transactions on Automatic Control. 2019 Jan 1;64(1):238–53.
Ivanov, R., et al. “Continuous estimation using context-dependent discrete measurements.” IEEE Transactions on Automatic Control, vol. 64, no. 1, Jan. 2019, pp. 238–53. Scopus, doi:10.1109/TAC.2018.2797839.
Ivanov R, Atanasov N, Pajic M, Weimer J, Pappas GJ, Lee I. Continuous estimation using context-dependent discrete measurements. IEEE Transactions on Automatic Control. 2019 Jan 1;64(1):238–253.

Published In

IEEE Transactions on Automatic Control

DOI

EISSN

1558-2523

ISSN

0018-9286

Publication Date

January 1, 2019

Volume

64

Issue

1

Start / End Page

238 / 253

Related Subject Headings

  • Industrial Engineering & Automation
  • 4007 Control engineering, mechatronics and robotics
  • 0913 Mechanical Engineering
  • 0906 Electrical and Electronic Engineering
  • 0102 Applied Mathematics