A Kalman Filter for Nonlinear Attitude Estimation Using Time Variable Matrices and Quaternions
Álvaro Deibe1, José Augusto Antón Nacimiento2, Jesús Cardenal2
1Integrated Group for Engineering Research, University of A Coruña, Mendizábal s/n, 15403 Ferrol, Spain.
Sensors (Basel, Switzerland)
|December 1, 2020
Summary
This study introduces a novel Kalman Filter for solid body attitude sensing. It simplifies calculations by using quaternions, time-varying matrices, and a unique state vector derived from measurable quantities.
Area of Science:
- Aerospace Engineering
- Control Systems
- Robotics
Background:
- Accurate attitude estimation is crucial for autonomous systems.
- Traditional methods often involve complex calculations like Jacobian matrices.
- Nonlinear dynamics present challenges in attitude sensing.
Purpose of the Study:
- To develop a simplified yet effective attitude estimation method.
- To address the nonlinear problem of solid body attitude sensing.
- To improve the computational efficiency of attitude estimators.
Main Methods:
- Implementation of a novel Kalman Filter approach.
- Utilizing quaternions for attitude representation.
- Incorporating time-varying matrices for dynamic modeling.
- Designing a state vector from measurable physical quantities.
Main Results:
- A novel attitude estimator preserving Kalman Filter simplicity.
- Avoidance of explicit Jacobian matrix calculations.
- Successful estimation from filter inputs and outputs.
- Effective handling of nonlinear dynamics.
Conclusions:
- The proposed method offers a computationally efficient attitude estimation solution.
- This novel Kalman Filter implementation simplifies complex nonlinear problems.
- The estimator is suitable for applications requiring robust attitude sensing.
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