Open Access Open Access  Restricted Access Subscription Access

A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation


Affiliations
1 Department of Mechanical and Aerospace Engineering (MAE) at West Virginia University (WVU), Morgantown, WV 26506, United States
2 Division of Engineering, Pennsylvania State University, Reading, PA 19610, United States
 

A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF) is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI) scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver's position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV) research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm.
User
Notifications
Font Size

Abstract Views: 73

PDF Views: 1




  • A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation

Abstract Views: 73  |  PDF Views: 1

Authors

Yu Gu
Department of Mechanical and Aerospace Engineering (MAE) at West Virginia University (WVU), Morgantown, WV 26506, United States
Jason N. Gross
Department of Mechanical and Aerospace Engineering (MAE) at West Virginia University (WVU), Morgantown, WV 26506, United States
Matthew B. Rhudy
Division of Engineering, Pennsylvania State University, Reading, PA 19610, United States
Kyle Lassak
Department of Mechanical and Aerospace Engineering (MAE) at West Virginia University (WVU), Morgantown, WV 26506, United States

Abstract


A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF) is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI) scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver's position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV) research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm.