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Sensors 2018, 18(2), 488; https://doi.org/10.3390/s18020488

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

1
School of Automatics, Northwestern Polytechnical University, Xi’an 710072, China
2
School of Engineering, RMIT University, Bundoora, VIC 3083, Australia
*
Author to whom correspondence should be addressed.
Received: 4 January 2018 / Revised: 1 February 2018 / Accepted: 3 February 2018 / Published: 6 February 2018
(This article belongs to the Special Issue Integrated Sensors)
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Abstract

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. View Full-Text
Keywords: multi-sensor data fusion; adaptive fading unscented Kalman filter; process-modeling error; Mahalanobis distance; linear minimum variance multi-sensor data fusion; adaptive fading unscented Kalman filter; process-modeling error; Mahalanobis distance; linear minimum variance
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Gao, B.; Hu, G.; Gao, S.; Zhong, Y.; Gu, C. Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter. Sensors 2018, 18, 488.

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