A sensor fusion system using enhanced extended Kalman filter with double fuzzy logics for autonomous robot guidance

2011 ◽  
Author(s):  
Seung-Hwan Lee ◽  
Tae-Seok Lee ◽  
Beom-Hee Lee
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Autonomous Robot ◽  
Fusion System ◽  
Fuzzy Logics

scholarly journals Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4126 ◽  
Author(s):  
Taeklim Kim ◽  
Tae-Hyoung Park
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Reliability Function ◽  
Position Estimation ◽  
Position Tracking ◽  
Radar Sensors ◽  
Vehicle Position ◽  
Sensor Errors ◽  
Fuzzy Adaptive

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.

scholarly journals Ship-to-Ship State Observer Using Sensor Fusion and the Extended Kalman Filter

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Sondre Sanden Tørdal ◽  
Geir Hovland
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Relative Motion ◽  
Degrees Of Freedom ◽  
Load Transfer ◽  
Real Life ◽  
Fusion Algorithm ◽  
Six Degrees Of Freedom ◽  
Motion Estimate

In this paper, a solution for estimating the relative position and orientation between two ships in six degrees-of-freedom (6DOF) using sensor fusion and an extended Kalman filter (EKF) approach is presented. Two different sensor types, based on time-of-flight and inertial measurement principles, were combined to create a reliable and redundant estimate of the relative motion between the ships. An accurate and reliable relative motion estimate is expected to be a key enabler for future ship-to-ship operations, such as autonomous load transfer and handling. The proposed sensor fusion algorithm was tested with real sensors (two motion reference units (MRS) and a laser tracker) and an experimental setup consisting of two Stewart platforms in the Norwegian Motion Laboratory, which represents an approximate scale of 1:10 when compared to real-life ship-to-ship operations.

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