Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances
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School of Aeronautics and Astronautics, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
2
Department of Mechanical Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(7), 1526; https://doi.org/10.3390/s17071526
Received: 26 April 2017
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Revised: 20 June 2017
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Accepted: 23 June 2017
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Published: 29 June 2017
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications 2017)
As the largest ellipsoid (LE) data fusion algorithm can only be applied to two-sensor system, in this contribution, parallel fusion structure is proposed to introduce the LE algorithm into a multisensor system with unknown cross-covariances, and three parallel fusion structures based on different estimate pairing methods are presented and analyzed. In order to assess the influence of fusion structure on fusion performance, two fusion performance assessment parameters are defined as Fusion Distance and Fusion Index. Moreover, the formula for calculating the upper bounds of actual fused error covariances of the presented multisensor LE fusers is also provided. Demonstrated with simulation examples, the Fusion Index indicates fuser’s actual fused accuracy and its sensitivity to the sensor orders, as well as its robustness to the accuracy of newly added sensors. Compared to the LE fuser with sequential structure, the LE fusers with proposed parallel structures not only significantly improve their properties in these aspects, but also embrace better performances in consistency and computation efficiency. The presented multisensor LE fusers generally have better accuracies than that of covariance intersection (CI) fusion algorithm and are consistent when the local estimates are weakly correlated.
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Keywords:
largest ellipsoid; distributed data fusion; parallel structure; unknown cross-covariances; multisensor
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MDPI and ACS Style
Liu, B.; Zhan, X.; Zhu, Z.H. Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances. Sensors 2017, 17, 1526. https://doi.org/10.3390/s17071526
AMA Style
Liu B, Zhan X, Zhu ZH. Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances. Sensors. 2017; 17(7):1526. https://doi.org/10.3390/s17071526
Chicago/Turabian StyleLiu, Baoyu, Xingqun Zhan, and Zheng H. Zhu. 2017. "Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances" Sensors 17, no. 7: 1526. https://doi.org/10.3390/s17071526
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