<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The fusion under unknown correlations tunes a combination of local estimates in such a way that upper bounds of the admissible mean square error matrices are optimised. Based on the recently discovered relation between the admissible matrices and Minkowski sums of ellipsoids, the optimality of existing algorithms is analysed. Simple examples are used to indicate the reasons for the suboptimality of the covariance intersection fusion of multiple estimates. Further, an extension of the existing family of upper bounds is proposed, which makes it possible to get closer to the optimum, and a general case is discussed. All results are obtained analytically and illustrated graphically.</p></abstract>

The fusion under unknown correlations tunes a combination of local estimates in such a way that upper bounds of the admissible mean square error matrices are optimised. Based on the recently discovered relation between the admissible matrices and Minkowski sums of ellipsoids, the optimality of existing algorithms is analysed. Simple examples are used to indicate the reasons for the suboptimality of the covariance intersection fusion of multiple estimates. Further, an extension of the existing family of upper bounds is proposed, which makes it possible to get closer to the optimum, and a general case is discussed. All results are obtained analytically and illustrated graphically.

Fusion of Multiple Estimates by Covariance Intersection: Why and Howit Is Suboptimal

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The fusion under unknown correlations tunes a combination of local estimates in such a way that upper bounds of the admissible mean square error matrices are...

Fusion of Multiple Estimates by Covariance Intersection: Why and Howit Is Suboptimal

Pubblicato online: 03 ott 2018

Pagine: 521 - 530

Ricevuto: 16 ago 2017

Accettato: 27 apr 2018

DOI: https://doi.org/10.2478/amcs-2018-0040

Parole chiave
decentralised estimation, fusion under unknown correlations, covariance intersection, Minkowski sum

© 2018 Jiří Ajgl, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Fusion of Multiple Estimates by Covariance Intersection: Why and Howit Is Suboptimal

Pubblicato online: 03 ott 2018

Pagine: 521 - 530

Ricevuto: 16 ago 2017

Accettato: 27 apr 2018

DOI: https://doi.org/10.2478/amcs-2018-0040

Parole chiavedecentralised estimation, fusion under unknown correlations, covariance intersection, Minkowski sum

© 2018 Jiří Ajgl, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
decentralised estimation, fusion under unknown correlations, covariance intersection, Minkowski sum