Journal ArticleDOI
Sequential Piecewise Recursive Filter for GPS Low-Dynamics Navigation
TLDR
The design, implementation, and performance of a real-time estimation algorithm, referred to in this paper as the sequential piecewise recursive (SPWR) algorithm, for the global-positioning system (GPS) low-dynamics navigation system is described.Abstract:
The design, implementation, and performance of a real-time estimation algorithm, referred to in this paper as the sequential piecewise recursive (SPWR) algorithm, for the global-positioning system (GPS) low-dynamics navigation system is described. The SPWR algorithm for this application was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented covariance and filter gains at a slower rate than the state measurement update, and it uses U-D factor formulation to perform covariance computations. The SPWR algorithm saves real-time processing requirements without appreciable degradation of filter performance. Another important feature of the SPWR algorithm is that it incorporates pseudorange and delta-range data from each GPS satellite sequentially for navigation solution. The SPWR algorithm, for this application, was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented.read more
Citations
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Journal ArticleDOI
Multiconfiguration Kalman filter design for high-performance GPS navigation
TL;DR: In this article, a real-time multiconfiguration Kalman filter for high-performance Navstar global positioning system (GPS) navigation is described, which configures automatically (four filter configurations) based upon the host vehicle requirements and sensor availability, to process GPS measurements and provide the best estimate of the navigation states.
Journal ArticleDOI
On the Orbit Determination Problem
J.R. Raol,N.K. Sinha +1 more
TL;DR: In this paper, a review of the available literature on the application of Kalman-filter type algorithms to the problem of near-Earth, geosynchronous, and deep-space mission type orbit determination is presented.
Kalman Filter Bibliography: Agriculture, Biology, and Medicine
Walter T. Federer,B. R. Murty +1 more
TL;DR: This bibliography on the Kalman filter was obtained from the Cornell University Library Resources computer network and the four indexes used were Agricola, Biosis pre-1993, Biotic post-1992, and CAB.
Journal ArticleDOI
Isolating errors in models of complex systems
J.L. Maryak,Mark S. Asher +1 more
TL;DR: An error isolation technique for detecting the misspecified parameter (or set of parameters) is described and is especially designed for use on state-space models of large-scale systems.
Journal ArticleDOI
Further comments on "Optimal sensor selection strategy for discrete-time estimators" [and reply]
T.H. Kerr,Y. Oshman +1 more
TL;DR: In this article, the TPBVP usually underlying the true "optimal sensor selection strategy" is revisited to obtain practical real-time mechanizations as a solution to an exclusively initial value problem.
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
Fast triangular formulation of the square root filter.
TL;DR: In this paper, a triangular formulation of the square root Kalman filter is presented, and an efficient analytic algorithm is derived for maintaining the covariance square root matrix in triangular form during the incorporation of measurements.
Journal ArticleDOI
A Comparison of Discrete Linear Filtering Algorithms
TL;DR: Seven filter algorithms were presented in a recent survey paper, and were compared computationally (operations count) when relatively few observations were to be processed, and it is shown that for problems with even moderately large amounts of data, the information matrix and square-root information matrix formulations are computationally more efficient than the other methods considered.
Proceedings ArticleDOI
Gram-Schmidt algorithms for covariance propagation
TL;DR: The U-D time update is shown to be less costly than the Schmidt method; and, except in unusual circumstances, it is within 20% of the cost of conventional propagation.
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