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Title: Continuous Estimation Using Context-Dependent Discrete Measurements
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 infor- mation, 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 approx- imating 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.  more » « less
Award ID(s):
1652544 1505701
PAR ID:
10056954
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
IEEE Transactions on Automatic Control
ISSN:
0018-9286
Page Range / eLocation ID:
1 to 1
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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