Journal ArticleDOI
Robust Kalman filtering for small satellite attitude estimation in the presence of measurement faults
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TLDR
A Robust Kalman filtering method is proposed for the attitude estimation problem and two new algorithms, which are robust against measurement malfunctions, are called Robust Extended Kalman Filter and Robust Unscented Kalman filter, respectively.About:
This article is published in European Journal of Control.The article was published on 2014-03-01. It has received 73 citations till now. The article focuses on the topics: Extended Kalman filter & Fast Kalman filter.read more
Citations
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Journal ArticleDOI
Robust unscented Kalman filter with adaptation of process and measurement noise covariances
TL;DR: A robust Masreliez-Martin UKF is presented which can provide reliable state estimates in the presence of both unknown process noise and measurement noise covariance matrices and can provide improved state estimation performance over existing robust filtering approaches.
Journal ArticleDOI
GPS/UWB/MEMS-IMU tightly coupled navigation with improved robust Kalman filter
TL;DR: The results indicate that the improved robust Kalman filter used in GPS/UWB/INS tightly coupled navigation is able to remove the harmful effect of gross error in UWB observation.
Journal ArticleDOI
Gyro-free attitude and rate estimation for a small satellite using SVD and EKF
TL;DR: A gyroless attitude determination system that can rely on magnetometer and sun sensor measurements and achieve good accuracy and can be used for low-cost small satellites where using high power consuming, expensive, and fragile gyroscopes for determining spacecraft attitude are not reasonable.
Journal ArticleDOI
Review on gyroless attitude determination methods for small satellites
TL;DR: In this paper, two kinds of gyroless satellite attitude determination algorithms were reviewed namely, vector measurements and Kalman filter based methods, and robust versions of those Kalman filters, which were incorporated with single, and multiple measurement noise scale factors (SMNSF, MMNSF respectively) are investigated and compared in the presence of measurement faults.
Fault Diagnosis And Reconfiguration In Flight Control Systems
TL;DR: Fault diagnosis and reconfiguration in flight control systems is downloaded for free for people facing with some malicious virus inside their desktop computer.
References
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Journal ArticleDOI
A new method for the nonlinear transformation of means and covariances in filters and estimators
TL;DR: A new approach for generalizing the Kalman filter to nonlinear systems is described, which yields a filter that is more accurate than an extendedKalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter.
BookDOI
Spacecraft attitude determination and control
TL;DR: In this paper, the first comprehensive presentation of data, theory, and practice in attitude analysis is presented, including orthographic globe projections to eliminate confusion in vector drawings and a presentation of new geometrical procedures for mission analysis and attitude accuracy studies which can eliminate many complex simulations.
Proceedings ArticleDOI
A new approach for filtering nonlinear systems
TL;DR: A new recursive linear estimator for filtering systems with nonlinear process and observation models which can be transformed directly by the system equations to give predictions of the transformed mean and covariance is described.
Spacecraft attitude determination and control
TL;DR: In this article, the first comprehensive presentation of data, theory, and practice in attitude analysis is presented, including orthographic globe projections to eliminate confusion in vector drawings and a presentation of new geometrical procedures for mission analysis and attitude accuracy studies which can eliminate many complex simulations.
Journal ArticleDOI
On the identification of variances and adaptive Kalman filtering
TL;DR: In this paper, it was shown that the steady-state optimal Kalman filter gain depends only on n \times r linear functionals of the covariance matrix and the number of unknown elements in the matrix.