Efficient Multi-Sensor Data Fusion for Space Surveillance
Multi-sensor networks can extend the sensing region of a single sensor in order to provide a more geometrically diverse and comprehensive view of the state of a dynamical system. The use of a multi-sensor network gives rise to the need for a fusion step that combines the outputs of all sensor nodes into a single probabilistic state description. This paper examines a fusion method based on logarithmic opinion pools and develops algorithms for multi-sensor data fusion as well as investigates weight selection schemes for the opinion pool using efficient quadrature integration methods. The proposed fusion rules are applied to the tracking of a space object using multiple ground-based optical sensors. It is shown that the multi-sensor fusion rule leads to an increase of nearly two orders of magnitude in the position tracking accuracy as compared to the traditional single-sensor tracking method.
K. J. DeMars et al., "Efficient Multi-Sensor Data Fusion for Space Surveillance," Proceedings of the American Control Conference (2015, Chicago, IL), vol. 2015-July, pp. 5212-5217, Institute of Electrical and Electronics Engineers (IEEE), Jul 2015.
The definitive version is available at https://doi.org/10.1109/ACC.2015.7172153
American Control Conference (2015: Jul. 1-3, Chicago, IL)
Mechanical and Aerospace Engineering
Keywords and Phrases
Data fusion; Dynamical systems; Sensor networks; Sensor nodes; Space surveillance; Tracking (position); Integration method; Multi-sensor fusion; Multi-sensor networks; Multiple grounds; Multisensor data fusion; Orders of magnitude; Selection scheme; Tracking method; Sensor data fusion
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International Standard Serial Number (ISSN)
Article - Conference proceedings
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