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Bierman, G. J.

Publications and source records attributed to Bierman, G. J..

At least 37 records · Page 2
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NASA NTRS1976

Additional comments on 'Multistage least-squares parameter estimation'

The purpose of this correspondence is to call attention to other solutions to the problem addressed in the above paper, that of adding or deleting parameters or measurements from a given linear model. The solution algorithms proposed in the article are numerically ill-conditioned and are less efficient, in terms of both computational and storage requirements, than the methods described here. Several numerically stable, compact, flexible, and efficient algorithms for analyzing the parameter estimation problem are described and others are referenced.

Bierman, G. J.↗
NASA NTRS1976

Integration-free interval doubling for Riccati equation solutions

Various algorithms are given for the case of constant coefficients. The algorithms are based on two ideas: first, relate the Re solution with general initial conditions to anchored RE solutions; and second, when the coefficients are constant the anchored solutions have a basic shift-invariance property. These ideas are used to construct an integration free superlinearly convergent iterative solution to the algebraic RE. The algorithm, arranged in square-root form, is thought to be numerically stable and competitive with other methods of solving the algebraic RE.

Bierman, G. J.↗
NASA NTRS1976

A numerical comparison of discrete Kalman filtering algorithms - An orbit determination case study

An improved Kalman filter algorithm based on a modified Givens matrix triangularization technique is proposed for solving a nonstationary discrete-time linear filtering problem. The proposed U-D covariance factorization filter uses orthogonal transformation technique; measurement and time updating of the U-D factors involve separate application of Gentleman's fast square-root-free Givens rotations. Numerical stability and accuracy of the algorithm are compared with those of the conventional and stabilized Kalman filters and the Potter-Schmidt square-root filter, by applying these techniques to a realistic planetary navigation problem (orbit determination for the Saturn approach phase of the Mariner Jupiter-Saturn Mission, 1977). The new algorithm is shown to combine the numerical precision of square root filtering with the efficiency of the original Kalman algorithm.

Thornton, C. L.↗
NASA NTRS1976

A numerical comparison of discrete Kalman filtering algorithms: An orbit determination case study

The numerical stability and accuracy of various Kalman filter algorithms are thoroughly studied. Numerical results and conclusions are based on a realistic planetary approach orbit determination study. The case study results of this report highlight the numerical instability of the conventional and stabilized Kalman algorithms. Numerical errors associated with these algorithms can be so large as to obscure important mismodeling effects and thus give misleading estimates of filter accuracy. The positive result of this study is that the Bierman-Thornton U-D covariance factorization algorithm is computationally efficient, with CPU costs that differ negligibly from the conventional Kalman costs. In addition, accuracy of the U-D filter using single-precision arithmetic consistently matches the double-precision reference results. Numerical stability of the U-D filter is further demonstrated by its insensitivity of variations in the a priori statistics.

Thornton, C. L.↗
NASA NTRS1976

Numerical comparison of discrete Kalman filter algorithms - Orbit determination case study

Numerical characteristics of various Kalman filter algorithms are illustrated with a realistic orbit determination study. The case study of this paper highlights the numerical deficiencies of the conventional and stabilized Kalman algorithms. Computational errors associated with these algorithms are found to be so large as to obscure important mismodeling effects and thus cause misleading estimates of filter accuracy. The positive result of this study is that the U-D covariance factorization algorithm has excellent numerical properties and is computationally efficient, having CPU costs that differ negligibly from the conventional Kalman costs. Accuracies of the U-D filter using single precision arithmetic consistently match the double precision reference results. Numerical stability of the U-D filter is further demonstrated by its insensitivity to variations in the a priori statistics.

Bierman, G. J.↗
NASA NTRS1975

Fixed memory least squares filtering

Buxbaum has reported on three algorithms for computing least squares estimates that are based on fixed amounts of data. In this correspondence, the filter is arranged as a point-deleting Kalman filter concatenated with the standard point-inclusion Kalman filter. The resulting algorithm is couched in a square root framework for greater numerical stability, and special attention is given to computer implementation.

Bierman, G. J.↗
NASA NTRS1975

Sequential Least-Squares Using Orthogonal Transformations

Square root information estimation, starting from its beginnings in least-squares parameter estimation, is considered. Special attention is devoted to discussions of sensitivity and perturbation matrices, computed solutions and their formal statistics, consider-parameters and consider-covariances, and the effects of a priori statistics. The constant-parameter model is extended to include time-varying parameters and process noise, and the error analysis capabilities are generalized. Efficient and elegant smoothing results are obtained as easy consequences of the filter formulation. The value of the techniques is demonstrated by the navigation results that were obtained for the Mariner Venus-Mercury (Mariner 10) multiple-planetary space probe and for the Viking Mars space mission.

Bierman, G. J.↗
NASA NTRS1975

The treatment of bias in the square-root information filter/smoother

The Dyer-McReynolds square-root information filter (SRIF) is rederived, using recursive least-square arguments. The result is applied to a system composed partly of biases. The filter sensitivity matrix, computed covariance, and consider covariance for this augmented system are reviewed. A new computationally attractive representation for the smoothed estimates, in terms of a smoothed sensitivity matrix and a smoothed computed covariance is presented.

Bierman, G. J.↗
NASA NTRS1975

Some estimation formulae for continuous time-invariant linear systems

In this brief paper we examine a Riccati equation decomposition due to Reid and Lainiotis and apply the result to the continuous time-invariant linear filtering problem. Exploitation of the time-invariant structure leads to integration-free covariance recursions which are of use in covariance analyses and in filter implementations. A super-linearly convergent iterative solution to the algebraic Riccati equation (ARE) is developed. The resulting algorithm, arranged in a square-root form, is thought to be numerically stable and competitive with other ARE solution methods. Certain covariance relations that are relevant to the fixed-point and fixed-lag smoothing problems are also discussed.

Bierman, G. J.↗
NASA NTRS1975

Measurement updating using the U-D factorization

A new mechanization of the Kalman updating algorithm based on a U-D factorization of the estimate error covariance is introduced. Efficient and stable updating recursions are developed for the unit upper triangular factor U and the diagonal factor D, treating only the parameter estimation problem. Properties of the factorization update performed here include efficient one point at a time processing that requires little more computation than the optimal but numerically unstable conventional Kalman measurement update algorithm, and stability that compares with the square root filter.

Bierman, G. J.↗
NASA NTRS1975

Gram-Schmidt algorithms for covariance propagation

This paper addresses the time propagation of triangular covariance factors. Attention is focused on the square-root free factorization, P = UDU/T/, where U is unit upper triangular and D is diagonal. An efficient and reliable algorithm for U-D propagation is derived which employs Gram-Schmidt orthogonalization. Partitioning the state vector to distinguish bias and colored process noise parameters increases mapping efficiency. Cost comparisons of the U-D, Schmidt square-root covariance and conventional covariance propagation methods are made using weighted arithmetic operation counts. 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.

Thornton, C. L.↗
NASA NTRS1974

Sequential square root filtering and smoothing of discrete linear systems

A square root information filter/smoother is derived using recursive least-squares arguments. The combined filter/smoother algorithm has the following attributes: (1) it has a square root structure, which enhances numerical accuracy; (2) filter and smoother mechanizations are identical in form, facilitating implementation of the smoother; and (3) storage and computation requirements are modest compared with other smoothing algorithms. Partitioning the results to separate bias parameters provides further computational economies and reduction of storage requirements.

Bierman, G. J.↗
NASA NTRS1973

A square-root data array solution of the continuous-discrete filtering problem

The Dyer-McReynolds (1969) discrete square-root filtering algorithm is extended to accommodate continuous dynamics. Differential equations are given to represent the time evolution of the filter data array. These equations are nonlinear, but it is shown that the nonlinearities act to enhance the stability of the solution.

Bierman, G. J.↗
NASA NTRS1973

A comparison of discrete linear filtering algorithms.

Seven filter algorithms were presented in a recent survey paper (Kaminski, 1971), and were compared computationally (operations count) when relatively few observations were to be processed. These algorithms are now elaborated further. Details of the computations are presented, 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 (conventional Kalman, stabilized Kalman, and square-root covariance mechanizations). It is pointed out that Schmidt's matrix factorization-Householder transformation technique leads to the same equations as those obtained via Potter's method. Several improvements in the equation mechanization are given.

Bierman, G. J.↗
NASA NTRS1972

Power series evaluation of transition and covariance matrices.

Reexamination power series solutions to the matrix covariance differential equation and the transition differential equation. Truncation error bounds are derived which are computationally attractive and which extend previous results. Polynomial approximations are obtained by exploiting the functional equations satisfied by the transition and covariance matrices. The series-functional equation propagation technique represents a fast and accurate alternative to the numerical integration of the time-invariant transition and covariance equations.

Bierman, G. J.↗
NASA NTRS1972

Weighted least squares stationary approximations to linear systems.

Investigation of the problem of replacing a certain time-varying linear system by a stationary one. Several quadratic criteria are proposed to aid in determining suitable candidate systems. One criterion for choosing the matrix B (in the stationary system) is initial-condition dependent, and another bounds the 'worst case' homogeneous system performance. Both of these criteria produce weighted least square fits.

Bierman, G. J.↗