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Online evaluation of the process noise covariance matrix for event‐based state estimators
Author(s) -
MartínezRey Miguel,
Santos Carlos,
Nieto Rubén,
Losada Cristina,
Espinosa Felipe
Publication year - 2019
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.6131
Subject(s) - covariance matrix , estimator , covariance , noise (video) , estimation of covariance matrices , algorithm , computer science , cma es , process (computing) , event (particle physics) , covariance intersection , state vector , mathematical optimization , mathematics , statistics , artificial intelligence , physics , quantum mechanics , classical mechanics , image (mathematics) , operating system
Summary In state estimation, adjusting the process noise covariance matrix is an important and often difficult task. Well‐known methods use the innovation vector to perform an adaptive adjustment, but when using event‐based sensors, the innovation vector is not available for the estimator. We propose an online method for adjusting the process noise covariance matrix using the expected and observed event rates, which is based on the golden section search optimization algorithm. Simulation results confirm the suitability and efficiency of our proposed method. The process noise covariance parameter converges to the actual covariance iteratively, reducing the sensor transmission rate and the estimation error.