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

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Gu ◽  
Jason N. Gross ◽  
Matthew B. Rhudy ◽  
Kyle Lassak
Keyword(s):  
Sensor Fusion ◽  
Fault Tolerant ◽  
Attitude Estimation ◽  
Single Frequency ◽  
Test Bed ◽  
Multiple Sources ◽  
Fusion Algorithm ◽  
Cost Rate ◽  
Fault Detection And Identification ◽  
Multiple Sensor

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.

scholarly journals Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Matthew Rhudy ◽  
Yu Gu ◽  
Jason Gross ◽  
Marcello R. Napolitano
Keyword(s):  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Square Root ◽  
Fusion Algorithm ◽  
Matrix Square Root ◽  
Global Positioning ◽  
The Matrix ◽  
Aerial Vehicle ◽  
Diagonalization Method

Using an Unscented Kalman Filter (UKF) as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF), was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV). The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

3D Attitude Estimation in Indoor Environments with a Complementary Filter for the Special Orthogonal Group SO(3)

2013 ◽  
Vol 198 ◽  
pp. 153-158 ◽  
Author(s):  
Marcin Kmiecik ◽  
Krzysztof Sibilski
Keyword(s):  
Sensor Fusion ◽  
Orthogonal Group ◽  
Laser Scanner ◽  
Attitude Estimation ◽  
Practical Implementation ◽  
Indoor Environments ◽  
Test Bed ◽  
Complementary Filter ◽  
Satisfactory Quality

This paper presents a practical implementation of a complementary filter for 3D attitude estimation in an indoor setting. The structure of the filter is chosen basing on design principles described in [. It demonstrates the basic foundations of a complementary filter and proposes a simple and concise implementation for sensor fusion of data collected using an IMU and a Hokuyo URG 04LX laser scanner. Experimental results are shown for a handheld test bed satisfactory quality of estimation.

An efficient fault-tolerant sensor fusion algorithm for accelerometers

2013 ◽  
Author(s):  
O. Sarbishei ◽  
B. Nahill ◽  
A. Roshan Fekr ◽  
M. Janidarmian ◽  
K. Radecka ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Fault Tolerant ◽  
Fusion Algorithm

Robust fault-tolerant flight control of quadrotor against faults in multiple motors

2021 ◽  
pp. 095441002199954
Author(s):  
Dinesh D Dhadekar ◽  
S E Talole
Keyword(s):  
Flight Control ◽  
Tracking Control ◽  
Fault Tolerant ◽  
External Disturbances ◽  
Detection And Identification ◽  
Attitude Tracking ◽  
Fault Detection And Identification ◽  
Attitude Tracking Control ◽  
Disturbance Estimator

In this article, position and attitude tracking control of the quadrotor subject to complex nonlinearities, input couplings, aerodynamic uncertainties, and external disturbances coupled with faults in multiple motors is investigated. A robustified nonlinear dynamic inversion (NDI)-based fault-tolerant control (FTC) scheme is proposed for the purpose. The proposed scheme is not only robust against aforementioned nonlinearities, disturbances, and uncertainties but also tolerant to unexpected occurrence of faults in multiple motors. The proposed scheme employs uncertainty and disturbance estimator (UDE) technique to robustify the NDI-based controller by providing estimate of the lumped disturbance, thereby enabling rejection of the same. In addition, the UDE also plays the role of fault detection and identification module. The effectiveness and benefits of the proposed design are confirmed through 6-DOF simulations and experimentation on a 3-DOF Hover platform.

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