Journal ArticleDOI
Backward-Smoothing Extended Kalman Filter
TLDR
In this article, the authors generalized the iterated extended Kalman filter to solve a nonlinear smoothing problem for the current and past sample intervals using iterative numerical techniques, which is useful when nonlinearities might significantly degrade the accuracy or convergence reliability of other filters.Abstract:
The principle of the iterated extended Kalman filter has been generalized to create a new filter that has superior performance when the estimation problem contains severe nonlinearities. The new filter is useful when nonlinearities might significantly degrade the accuracy or convergence reliability of other filters. The new filter solves a nonlinear smoothing problem for the current and past sample intervals using iterative numerical techniques. This approach retains the nonlinearities of a fixed number of stages that precede the stage of interest, and it processes information from earlier stages in an approximate manner. The algorithm has been simulation tested on a difficult spacecraft attitude estimation problem that includes sensing of fewer than three axes and significant dynamic model uncertainty. The filter compensates for this uncertainty via simultaneous estimation of moment of inertia parameters. The new filter exhibits markedly better convergence reliability and accuracy than an extended Kalman filter and an unscented Kalman filter for estimation problems that start with large initial attitude or attitude rate errors.read more
Citations
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Journal ArticleDOI
Survey of nonlinear attitude estimation methods
TL;DR: A survey of modern nonlinear filtering methods for attitude estimation based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance is provided.
Journal ArticleDOI
Square-root quaternion cubature Kalman filtering for spacecraft attitude estimation
TL;DR: In this paper, a square-root quaternion cubature Kalman filter is proposed for spacecraft attitude estimation, which uses a gyro-based model for quaternions propagation and reduces the measurement model to substantially reduce computational costs.
Tightly-Coupled Opportunistic Navigation for Deep Urban and Indoor Positioning
TL;DR: A simple demonstration of the TCON strategy focused on timing shows that a TCONenabled receiver can characterize and use CDMA cellular signals to correct its local clock variations, allowing it to coherently integrate GNSS signals beyond 100 seconds.
Journal ArticleDOI
Covariance Correction Step for Kalman Filtering with an Attitude
TL;DR: In this paper, a reset step that adjusts the covariance matrix when information is moved from the attitude deviation to the reference attitude is derived, which allows one to easily construct a Kalman filter for a system for which the state includes an attitude.
Journal ArticleDOI
Satellite orbit determination using a batch filter based on the unscented transformation
TL;DR: In this paper, a non-recursive batch filter based on the unscented transformation is presented and utilized for satellite orbit determination, which yields more robust and stable convergence than the existing batch least squares filter.
References
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Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Book
Applied Optimal Estimation
TL;DR: This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation, and the theory and practice of optimal estimation is presented.
Book
Estimation with Applications to Tracking and Navigation
TL;DR: Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations using a balanced combination of linear systems, probability, and statistics.
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.