Unscented Filtering for Spacecraft Attitude Estimation

By John L. Crassidis, F. Landis, Markley. 2003

977 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Oceanography

View Paper: https://www.semanticscholar.org/paper/7478f3eda17149006533335e0ad4e321e245e168

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Citations in the corpus

An efficient orientation filter for inertial and inertial / magnetic sensor arrays (2010, background): “They can be complicated to implement which is reflected by the numerous solutions seen in the subject literature [3, 4, 15, 16, 17, 24, 25, 26, 27, 28, 29, 30, 31, 32].”
Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter (2006, background): “Crassidis and Markley [6] consider the UKF for spacecraft attitude estimation problems and report that for realistic conditions their UKF implementation consistently outperforms the EKF especially when large initialization errors are present.”
Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing (2011, methodology): “Oftentimes, this feature is exploited, especially for applications of air and spacecraft attitude estimation, so as to compensate the gyros before performing the numerical integration of kinematics equations [36,46,56].”
Covariance Correction Step for Kalman Filtering with an Attitude (2017, methodology): “In this case, the resulting algorithm would be a correction of the USQUE of [13].”
Ltd Registered in England and Wales Registered Number : 1072954 Registered office : (2004, positive): “The discrete-time equivalent of (82) augmented by the gyro drift random-walk model (Crassidis and Markley 2003) is given by”
Sigma-point Kalman filtering for integrated GPS and inertial navigation (2005, methodology): “where c is a parameter from 0 to 1, and f is a scale factor chosen to be f = 2(c+1) [4].”
Unscented Filtering in a Unit Quaternion Space for Spacecraft Attitude Estimation (2007, methodology): “It was shown that the performance of UKF outperforms that of the standard EKF for large initialization errors [5].”
A Numerical-Integration Perspective on Gaussian Filters (2006, background): “THE role of filtering in the practical systems—from spacecraft attitude estimation [1] to ballistic target tracking [2], [3] and precision agriculture [4], [5]—cannot be overemphasized at all.”
Sparse Gauss-Hermite Quadrature Filter with Application to Spacecraft Attitude Estimation (2011, methodology): “The orbit parameters used here are obtained from the Tropical Rainfall Measuring Mission spacecraft [3].”
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration (2011, methodology): “This approach, called the unscented quaternion estimator (USQUE) in Crassidis and Markely (2003), is similar to the algorithm described by Kraft (2003), but does not require an iterated gradient descent step.”