Injection-Constrained State Estimation

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Ankit ICSE FinalSubmission Dec2021.pdf (1.26 MB)

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https://arc.aiaa.org/doi/10.2514/1.G006108

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https://doi.org/10.2514/1.G006108
 http://hdl.handle.net/11603/24136

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UMBC Mechanical Engineering Department
UMBC Faculty Collection

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Author/Creator ORCID

https://orcid.org/0000-0002-4146-6275

Date

2022-01-11

Type of Work

journal articles
postprints
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Citation of Original Publication

Goel, Ankit and Dennis S. Bernstein. Injection-Constrained State Estimation. Journal of Guidance, Control, and Dynamics 0 0:0, 1-12. https://doi.org/10.2514/1.G006108.

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Abstract

In applications of state estimation involving data assimilation over a spatial region, it is often convenient, and sometimes necessary, to confine the state correction to a prescribed subspace of the state space that corresponds to the measurement location. This is the injection-constrained state-estimation problem, where the injection of the output-error is constrained to a specified subspace of the state space. Unlike full-state output-error injection, which is the dual of static full-state feedback, constrained output-error injection is the dual of static output feedback. To address the injection-constrained state-estimation problem, this paper develops the injectionconstrained unscented Kalman filter (IC-UKF) and the injection-constrained retrospective cost filter (IC-RCF). The performance of these filters is evaluated numerically for linear and nonlinear state-estimation problems in order to compare their accuracy and determine their suboptimality relative to full-state output-error injection. As a benchmark test case, IC-UKF and IC-RCF are applied to the viscous Burgers equation for state and parameter estimation.