Attitude estimation from Kalman filter using sensor fusion via data from a gyroscope and accelerometer, providing angular velocity and a reference direction (direction of gravity), respectively. In the third of my three-part series, we first go over a MATLAB example of estimating position and velocity from noisy position data (altitude from sonar). We compare the estimated velocity with that from a finite-difference estimate and find the Kalman filter's is much smoother.
Finally, we discuss the problem of estimating attitude, and formulating the system model. We find that 3-2-1 Euler angles (yaw, pitch, and roll) are not a good state variable x as their evolution equation is not linear in x. However, quaternions q (i.e., Euler parameters ß) are a good attitude representation because their kinematic dynamical equation is linear. A gyroscope provides the angular velocity, ω, as measured in the body-fixed frame for a vehicle, spacecraft, or aircraft. Combining ω with a reference direction g that provides roll and pitch, we demonstrate the use of sensor fusion (from a gyroscope and accelerometer, respectively).
For both examples, construct a system model via the state transition matrix A, the state-to-measurement matrix H, and estimate the process noise and measurement noise matrices, Q and R.
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This special lecture series takes us into dynamic attitude estimation, using time-varying gyroscope data, as opposed to the previously covered *static* attitude estimation, which uses simultaneous measurements of known external objects.
Attitude Determination And Control System (ADCS)
► *Next: Moment of Inertia Matrix & Principal Axis Frame | MATLAB Example Calculation*
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► *Previous, Kalman Filter for Beginners, Part 2 - Estimation and Prediction Process & MATLAB Example*
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► *Prof. Shane Ross* 🌠 aerospace engineering professor, Virginia Tech
Background: Caltech PhD | worked at NASA/JPL & Boeing
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► Space Vehicle Dynamics course videos (playlist)
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► Lecture notes for Kalman Filter series (PDF)
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► MATLAB Code
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► Reference
Kalman Filter for Beginners: with MATLAB Examples
by Phil Kim (Author), Lynn Huh (Translator), 2010
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► Inertial Measurement Unit Used (IMU Sensor)
WIT Motion, Gyroscope + Accelerometer + Magnetometer
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► Chapters
0:00 Estimating Velocity From Position using Kalman Filter
5:08 Comparison with Finite Differences Approximation for Velocity
9:00 Dynamic Attitude Determination
9:53 WIT Motion Sensor
12:16 Integrating Gyroscope Angular Velocities from Sensor, MATLAB
18:00 Kalman Filter using Yaw, Pitch, Roll Euler Angles
20:30 Kalman Filter using Quaternions (Euler Parameters)
26:16 MATLAB Demo Using Quaternions
30:00 Data Fusion - Accelerometer with Gyroscope
39:01 Sensor Data Fusion Recap
► *Courses & Playlists by Dr. Ross*
📚Space Vehicle Dynamics
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📚3-Body Problem Orbital Dynamics Course
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📚Space Manifolds
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📚Lagrangian and 3D Rigid Body Dynamics
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📚Nonlinear Dynamics and Chaos
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📚Hamiltonian Dynamics
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📚Center Manifolds, Normal Forms, and Bifurcations
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