Adaptive Variational Bayesian Cubature Quadrature Kalman Filter With Randomly Delayed Measurements and Unknown Noise Covariance Matrices.
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| Title: | Adaptive Variational Bayesian Cubature Quadrature Kalman Filter With Randomly Delayed Measurements and Unknown Noise Covariance Matrices. |
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| Authors: | Mannarayana, Poluri Sri1 (AUTHOR) psm.19ee1101@phd.nitdgp.ac.in, Dey, Aritro2 (AUTHOR) |
| Source: | International Journal of Adaptive Control & Signal Processing. May2026, Vol. 40 Issue 5, p1118-1136. 19p. |
| Subjects: | Kalman filtering, Nonlinear estimation, Numerical integration, Bayesian analysis |
| Abstract: | This work presents a variational Bayesian (VB) adaptive quadrature Kalman filter, a new state estimation algorithm for nonlinear dynamic systems for a challenging situation where measurements face one‐step random delay and process as well as measurement noise covariance matrices remain unknown to the estimator. The random delay in the measurement is modeled as an independent Bernoulli random variable (BRV), and the proposed algorithm is subsequently formulated based on an exponential hierarchical Gaussian state‐space model of the likelihood function. In the absence of knowledge of the process and measurement noise covariances, the elements of predicted error covariance and measurement noise covariance are modeled using the inverse Wishart (IW) distribution which are jointly estimated along with the states with the newly designed VB‐based adaptive quadrature Kalman filter. The proposed algorithm is validated via simulation on a bearing‐only tracking (BoT) problem and a harmonic estimation problem using real‐time phase current data. Relative performance comparison of the proposed VB‐based algorithm with algorithms based on maximum likelihood estimation (MLE) and non‐adaptive approaches demonstrates the superiority of the proposed method. Furthermore, a comparative study of the VB‐based cubature quadrature Kalman filter (CQKF) against competing variants such as the unscented Kalman filter (UKF), cubature Kalman filter (CKF), and Gauss–Hermite filter (GHF) highlights the advantages of the proposed algorithm in terms of both performance and computational efficiency. The validation of the proposed algorithm with real measurements indicates the suitability of the proposed work for nonlinear state estimation. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Adaptive Control & Signal Processing is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193465694 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive Variational Bayesian Cubature Quadrature Kalman Filter With Randomly Delayed Measurements and Unknown Noise Covariance Matrices. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mannarayana%2C+Poluri+Sri%22">Mannarayana, Poluri Sri</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> psm.19ee1101@phd.nitdgp.ac.in</i><br /><searchLink fieldCode="AR" term="%22Dey%2C+Aritro%22">Dey, Aritro</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Adaptive+Control+%26+Signal+Processing%22">International Journal of Adaptive Control & Signal Processing</searchLink>. May2026, Vol. 40 Issue 5, p1118-1136. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+estimation%22">Nonlinear estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+integration%22">Numerical integration</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This work presents a variational Bayesian (VB) adaptive quadrature Kalman filter, a new state estimation algorithm for nonlinear dynamic systems for a challenging situation where measurements face one‐step random delay and process as well as measurement noise covariance matrices remain unknown to the estimator. The random delay in the measurement is modeled as an independent Bernoulli random variable (BRV), and the proposed algorithm is subsequently formulated based on an exponential hierarchical Gaussian state‐space model of the likelihood function. In the absence of knowledge of the process and measurement noise covariances, the elements of predicted error covariance and measurement noise covariance are modeled using the inverse Wishart (IW) distribution which are jointly estimated along with the states with the newly designed VB‐based adaptive quadrature Kalman filter. The proposed algorithm is validated via simulation on a bearing‐only tracking (BoT) problem and a harmonic estimation problem using real‐time phase current data. Relative performance comparison of the proposed VB‐based algorithm with algorithms based on maximum likelihood estimation (MLE) and non‐adaptive approaches demonstrates the superiority of the proposed method. Furthermore, a comparative study of the VB‐based cubature quadrature Kalman filter (CQKF) against competing variants such as the unscented Kalman filter (UKF), cubature Kalman filter (CKF), and Gauss–Hermite filter (GHF) highlights the advantages of the proposed algorithm in terms of both performance and computational efficiency. The validation of the proposed algorithm with real measurements indicates the suitability of the proposed work for nonlinear state estimation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Adaptive Control & Signal Processing is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/acs.70051 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1118 Subjects: – SubjectFull: Kalman filtering Type: general – SubjectFull: Nonlinear estimation Type: general – SubjectFull: Numerical integration Type: general – SubjectFull: Bayesian analysis Type: general Titles: – TitleFull: Adaptive Variational Bayesian Cubature Quadrature Kalman Filter With Randomly Delayed Measurements and Unknown Noise Covariance Matrices. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mannarayana, Poluri Sri – PersonEntity: Name: NameFull: Dey, Aritro IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08906327 Numbering: – Type: volume Value: 40 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Adaptive Control & Signal Processing Type: main |
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