Evolved Extended Kalman Filter for first-order dynamical systems with unknown measurements noise covariance
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{HERRERA:2022:ASC,
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author = "Leonardo Herrera and M. C. Rodriguez-Linan and
Eddie Clemente and Marlen Meza-Sanchez and
Luis Monay-Arredondo",
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title = "Evolved Extended {Kalman} Filter for first-order
dynamical systems with unknown measurements noise
covariance",
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journal = "Applied Soft Computing",
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year = "2022",
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volume = "115",
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pages = "108174",
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keywords = "genetic algorithms, genetic programming, Extended
Kalman Filter, Analytic behaviors, Nonlinear
first-order dynamical systems, Logistic map system",
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ISSN = "1568-4946",
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URL = "https://www.human-competitive.org/sites/default/files/humiesentry-eekf.txt",
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URL = "https://www.human-competitive.org/sites/default/files/papereekf.pdf",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494621010280",
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DOI = "doi:10.1016/j.asoc.2021.108174",
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size = "13 pages",
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abstract = "We focus on an open problem in the design of Extended
Kalman filters: the lack of knowledge of the
measurement noise covariance. A novel extension of the
analytic behaviors framework, which integrates a
theoretical formulation and evolutionary computing, has
been introduced as a design methodology for the
construction of this unknown parameter. The proposed
methodology is developed and applied for the design of
Evolved Extended Kalman Filters for nonlinear
first-order dynamical systems. The proposed methodology
applies an offline evolutionary synthesis of analytic
nonlinear functions, to be used as measurement noise
covariance, aiming to minimize the Kalman criterion.
The virtues of the methodology are exemplified through
a complex, highly nonlinear, first-order dynamical
system, for which 2649 optimised replacements of the
measurement noise covariance are found. Under different
scenarios, the performance of the Evolved Extended
Kalman Filter with unknown measurement noise covariance
is compared with that of the conventional Extended
Kalman Filter where the measurement noise covariance is
known. The robustness of the Evolved Extended Kalman
Filter is demonstrated through numerical evaluation",
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notes = "2022 HUMIES finalist.
Mechanical and Aerospace Engineering Department, NPS,
Monterey, USA",
- }
Genetic Programming entries for
Leonardo Herrera
Maria del Carmen Rodriguez-Linan
Eddie Helbert Clemente Torres
Marlen Meza-Sanchez
Luis Monay-Arredondo
Citations