Journal ArticleDOI
An extension of the SRIF Kalman filter
C. Boncelet,B. Dickinson +1 more
TLDR
In this paper, the problem of implementing the Kalman filter recursions in square root information filter form was considered and a general linear, dynamical model which directly incorporates the fact that many of the unknowns are not time varying was proposed.Abstract:
We consider the problem of implementing the Kalman filter recursions in square root information filter form. We suggest a general linear, dynamical model which directly incorporates the fact that many of the unknowns are not time varying. The resulting implementation is widely applicable, numerically sound, and extends easily to smoothing problems.read more
Citations
More filters
Journal ArticleDOI
Multiple sensor estimation using a new fifth-degree cubature information filter
TL;DR: A new class of cubature information filters is proposed for multiple sensor estimation that generalizes the conventional third-degree CIF to attain higher estimation accuracy using a class of higher-degree cubature integration rules including the fifth-degree Mysovskikh’s spherical rule and the arbitrary degree radial rule.
Journal ArticleDOI
Robust Kalman tracking and smoothing with propagating and non-propagating outliers
TL;DR: In this paper, the authors proposed robust recursive filters and smoothers for tracking in the presence of propagating outliers, and applied these procedures in the context of a GPS problem arising in the car industry.
Journal ArticleDOI
Robust Kalman tracking and smoothing with propagating and non-propagating outliers
TL;DR: This work proposes new robust recursive filters and smoothers designed for this purpose as well as specialized versions for non-propagating outliers, and applies these procedures in the context of a GPS problem arising in the car industry.
Proceedings ArticleDOI
Multiple sensor estimation using a high-degree cubature information filter
TL;DR: Astatistical linear error propagation method incorporates the high-degree cubature integration rule into the extended information filtering framework such that more accurate estimation can be achieved than the extendedInformation filter as well as the unscented information filter (UIF).
Journal ArticleDOI
Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems.
TL;DR: A practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented, which exploits the sparseness and/or symmetry of matrices to simplify the computational procedure.
References
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Journal ArticleDOI
Discrete square root filtering: A survey of current techniques
TL;DR: In this article, the square root approach is proposed to solve the problem of discrete filtering in the absence of a state estimate and an error covariance matrix from stage to stage, which is equivalent algebraically to the conventional Kalman approach.
Journal ArticleDOI
Linear Dynamic Recursive Estimation from the Viewpoint of Regression Analysis
David B. Duncan,Susan D. Horn +1 more
TL;DR: This work presents the relevant random-β regression theory as a natural extension of conventional fixed- β regression theory and derives the optimal recursive estimators in terms of the extended regression theory for a typical form of the recursive model.
Journal ArticleDOI
Square-root algorithms for least-squares estimation
Martin Morf,Thomas Kailath +1 more
TL;DR: Several new algorithms are presented, and more generally a new approach, to recursive estimation algorithms for linear dynamical systems, based on certain simple geometric interpretations of the overall estimation problem.
Journal ArticleDOI
Extension of square-root filtering to include process noise
P. Dyer,S. Mcreynolds +1 more
TL;DR: In this article, two different square root filters, one obtained by Potter and the other employing Householder transformations, are extended to include process noise, and the relative numerical accuracy of various sequential filters is explored with several examples.