A review of Kalman filter with artificial intelligence techniques

dc.contributor.authorKim, Sukkeun
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorShin, Hyosang
dc.date.accessioned2022-05-25T09:14:42Z
dc.date.available2022-05-25T09:14:42Z
dc.date.issued2022-05-12
dc.description.abstractKalman filter (KF) is a widely used estimation algorithm for many applications. However, in many cases, it is not easy to estimate the exact state of the system due to many reasons such as an imperfect mathematical model, dynamic environments, or inaccurate parameters of KF. Artificial intelligence (AI) techniques have been applied to many estimation algorithms thanks to the advantage of AI techniques that have the ability of mapping between the input and the output, the so-called "black box". In this paper, we found and reviewed 55 papers that proposed KF with AI techniques to improve its performance. Based on the review, we categorised papers into four groups according to the role of AI as follows: 1) Methods tuning parameters of KF, 2) Methods compensating errors in KF, 3) Methods updating state vector or measurements of KF, and 4) Methods estimating pseudo-measurements of KF. In the concluding section of this paper, we pointed out the directions for future research that suggestion to focus on more research for combining the categorised groups. In addition, we presented the suggestion of beneficial approaches for representative applications.en_UK
dc.identifier.citationKim S, Petrunin I, Shin H-S. (2022) A review of Kalman filter with artificial intelligence techniques. In: 2022 Integrated Communication. Navigation and Surveillance Conference (ICNS), 5-7 April 2022, Dulles, USAen_UK
dc.identifier.issn2155-4951
dc.identifier.urihttps://doi.org/10.1109/ICNS54818.2022.9771520
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17957
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleA review of Kalman filter with artificial intelligence techniquesen_UK
dc.typeConference paperen_UK

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