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

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

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 di...

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Bibliographic Details
Main Authors: Bingbing Gao, Gaoge Hu, Shesheng Gao, Yongmin Zhong, Chengfan Gu
Format: Article
Language:English
Published: MDPI AG 2018-02-01
Series:Sensors
Subjects:
multi-sensor data fusion
adaptive fading unscented Kalman filter
process-modeling error
Mahalanobis distance
linear minimum variance
Online Access:http://www.mdpi.com/1424-8220/18/2/488

Internet

http://www.mdpi.com/1424-8220/18/2/488

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