Fusion of Multiple Estimates by Covariance Intersection: Why and Howit Is Suboptimal-Reference-Cited by-同舟云学术

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

Published:2018-09-01 Issue:3 Volume:28 Page:521-530
ISSN:2083-8492
Container-title:International Journal of Applied Mathematics and Computer Science
language:en
Short-container-title:

Author:

Ajgl Jiří¹,Straka Ondřej²

Affiliation:

1. European Centre of Excellence—New Technologies for Information Society & Department of Cybernetics Faculty of Applied Sciences, University of West Bohemia Univerzitní 8, Pilsen , Czech Republic

2. European Centre of Excellence-New Technologies for Information Society & Department of Cybernetics Faculty of Applied Sciences, University of West Bohemia Univerzitní 8, Pilsen , Czech Republic

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Link

https://www.sciendo.com/pdf/10.2478/amcs-2018-0040

Reference24 articles.

1. Ajgl, J. and Šimandl, M. (2014). On linear estimation fusion under unknown correlations of estimator errors, Proceedings of the 19th IFAC World Congress, Cape Town, South Africa, pp. 2364-2369.

2. Ajgl, J., Šimandl, M., Reinhardt, M., Noack, B. and Hanebeck, U.D. (2014). Covariance Intersection in state estimation of dynamical systems, Proceedings of the 17th International Conference on Information Fusion, Salamanca, Spain.

3. Ajgl, J. and Straka, O. (2016a). Covariance Intersection in track-to-track fusion with memory, Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Baden-Baden, Germany, pp. 359-364.10.1109/MFI.2016.7849514

4. Ajgl, J. and Straka, O. (2016b). Covariance Intersection in track-to-track fusion without memory, Proceedings of the 19th International Conference on Information Fusion, Heidelberg, Germany.10.1109/MFI.2016.7849514

5. Ajgl, J. and Straka, O. (2017). A geometrical perspective on fusion under unknown correlations based on Minkowski sums, Proceedings of the 20th International Conference on Information Fusion, Xi’an, China, pp. 725-732.

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Artificial Neural Twin — Process optimization and continual learning in distributed process chains;Neural Networks;2024-12

2. A Quarter Century of Covariance Intersection: Correlations Still Unknown? [Lecture Notes];IEEE Control Systems;2024-04

3. New Insights into Largest Ellipsoid Estimation;2024 IEEE Aerospace Conference;2024-03-02

4. Fusion of Distance Measurements Between Agents With Unknown Correlations;IEEE Control Systems Letters;2023

5. A Study of Fusions of Multiple Estimates for Limit Cases;2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI);2022-09-20