Estimating and controlling UAV position using RGB-D/IMU data fusion with decentralized information/Kalman filter

2015 ◽  
Author(s):  
Milton C. P. Santos ◽  
Lucas V. Santana ◽  
Maria M. Martins ◽  
Alexandre S. Brandao ◽  
Mario Sarcinelli-Filho
Keyword(s):  
Kalman Filter ◽  
Data Fusion

Unscented Kalman Filter for INS/GNSS Data Fusion with Time Delay

2021 ◽  
Author(s):  
Kanishke Gamagedara ◽  
Taeyoung Lee ◽  
Murray R. Snyder
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Data Fusion ◽  
Unscented Kalman Filter ◽  
Gnss Data

Data Fusion via Kalman Filter: GPS and INS

2018 ◽  
pp. 99-148
Author(s):  
Jingrong Cheng ◽  
Yu Lu ◽  
Elmer R. Thomas ◽  
Jay A. Farrell
Keyword(s):  
Kalman Filter ◽  
Data Fusion

scholarly journals UAV Altitude Measurement Method Based on Data Fusion and Kalman Filter

2020 ◽  
Vol 1631 ◽  
pp. 012094
Author(s):  
Junjia Yang ◽  
Bing Qian ◽  
Erwei Zhang ◽  
Kun Qu
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Measurement Method

scholarly journals Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion

2019 ◽  
Vol 39 (4) ◽  
pp. 835-849 ◽  
Author(s):  
Jinshan Huang ◽  
Xianzhi Li ◽  
Xiongjun Yang ◽  
Zhupeng Zheng ◽  
Ying Lei
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Extended Kalman Filter ◽  
Experimental Validation ◽  
Structural Parameters ◽  
Wide Band ◽  
Unknown Inputs ◽  
Filter Methods ◽  
Filter Approach ◽  
Shear Building

The extended Kalman filter is a useful tool in the research of structural health monitoring and vibration control. However, the traditional extended Kalman filter approach is only applicable when the information of external inputs to structures is available. In recent years, some improved extended Kalman filter methods applied with unknown inputs have been proposed. The authors have proposed an extended Kalman filter with unknown inputs based on data fusion of partially measured displacement and acceleration responses. Compared with previous approaches, the drifts in the estimated structural displacements and unknown external inputs can be avoided. The feasibility of proposed extended Kalman filter with unknown inputs has been demonstrated by some numerical simulation examples. However, experimental validation of the proposed extended Kalman filter with unknown inputs has not been conducted. In this paper, an experiment is conducted to validate the effectiveness of the proposed approach. A five-story shear building model subjected to an unknown external excitation of wide-band white noise is conducted. Moreover, the data fusion of partially measured strain and acceleration responses from the building is adopted as it is difficult to accurately measure structural displacement in practice. Identified results show that the recently proposed extended Kalman filter with unknown inputs can be applied to identify structural parameters, structural states, and the unknown inputs in real time.

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