State estimation and multi-sensor data fusion using data-based neurofuzzy local linearisation process models

2001 ◽  
Vol 2 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Chris J. Harris ◽  
Qiang Gan
Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 919 ◽  
Author(s):  
Hao Du ◽  
Wei Wang ◽  
Chaowen Xu ◽  
Ran Xiao ◽  
Changyin Sun

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.


2019 ◽  
Vol 92 ◽  
pp. 109-118 ◽  
Author(s):  
Claudio M. de Farias ◽  
Luci Pirmez ◽  
Giancarlo Fortino ◽  
Antonio Guerrieri

Author(s):  
Geoffrey Ho ◽  
Erin Kim ◽  
Shahzaib Khattak ◽  
Stephanie Penta ◽  
Tharmarasa Ratnasingham ◽  
...  

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