Recursive state estimation - Unknown but bounded errors and system inputs.
State of linear dynamic system using noisy observations with unknown but bounded errors and system inputs, deriving recursive algorithm
Schweppe, F. C.↗
SEARCH · Search NASA
Results for “Error bounds”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Error bounds for the white Gaussian and other very noisy memoryless channels with generalized decision regions.
Error bounds for orthogonal signals in additive white Gaussian noise channels for class of generalized decision strategies permitting variable-size list decoding
Viterbi, A. J.↗
On error bounds in the digital computation of the four parameters for strapdown inertial systems
Numerical analysis of error bounds in digital computation of four parameters for strapdown inertial systems
Boland, J. S., III↗
An error bound for a discrete reduced order model of a linear multivariable system
The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.
Al-Saggaf, Ubaid M.↗
Automatic error bounds for real roots of polynomials having interval coefficients
Polynomials with interval coefficients, presenting Newton Raphson algorithm for real roots error bounds
Hanson, R. J.↗
Conjugate quasilinear Dirichlet and Neumann problems and a posteriori error bounds
Quasilinear Dirichlet and Neumann problems on a rectangle D with boundary D prime are considered. Using these concepts, conjugate problems, that is, a pair of one Dirichlet and one Neumann problem, the minima of the energies of which add to zero, are introduced. From the concept of conjugate problems, two-sided bounds for the energy of the exact solution of any given Dirichlet or Neumann problem are constructed. These two-sided bounds for the energy at the exact solution are in turn used to obtain a posteriori error bounds for the norm of the difference of the approximate and exact solutions of the problem. These bounds do not involve the unknown exact solution and are easily constructed numerically.
Lavery, J. E.↗
Probability-of-error bounds for binary transmission on the slowly fading Rician channel.
Chernoff bound and tilted distribution argument for obtaining error probability bounds for binary signaling on slowly fading Rician channel
SIGNAL PROCESSING↗
Mean-square-error bounds for reduced-order linear state estimators
The mean-square error of reduced-order linear state estimators for continuous-time linear systems is investigated. Lower and upper bounds on the minimal mean-square error are presented. The bounds are readily computable at each time-point and at steady state from the solutions to the Ricatti and the Liapunov equations. The usefulness of the error bounds for the analysis and design of reduced-order estimators is illustrated by a practical numerical example.
Baram, Y.↗
Quantification of model error via an interval model with nonparametric error bound
The quantification of model uncertainty is becoming increasingly important as robust control is an important tool for control system design and analysis. This paper presents an algorithm that effectively characterizes the model uncertainty in terms of parametric and nonparametric uncertainties. The algorithm utilizes the frequency domain model error which is estimated from the spectra of output error and input data. The parametric uncertainty is represented as an interval transfer function while the nonparametric uncertainty is bounded by a designed error bound transfer function. Both discrete and continuous systems are discussed in this paper. The algorithm is applied to the Mini-Mast example, and the detail analysis is given.
Lew, Jiann-Shiun↗
(abstract) Improvements on Probability of Error Bounds for Block Codes on the Gaussian Channel
We are interested in the problem of finding good codes for the Gaussian channel at low signal-to-noise ratio, and in estimating as accurately as possible their probability of error.
Gaussian channel error bounds Gaussian noise↗
Error bounds for multi-h phase codes
Upper and lower bounds for the probability of a decoding error event and for symbol error probability are developed for the class of time-varying phase codes known as multi-h codes. The effect of finite decoder memory is also treated. The analysis is illustrated with numerical examples, and simulation results are compared.
Wilson, S. G.↗
Combined Uncertainty and A-Posteriori Error Bound Estimates for CFD Calculations: Theory and Implementation
Simulation codes often utilize finite-dimensional approximation resulting in numerical error. Some examples include, numerical methods utilizing grids and finite-dimensional basis functions, particle methods using a finite number of particles. These same simulation codes also often contain sources of uncertainty, for example, uncertain parameters and fields associated with the imposition of initial and boundary data,uncertain physical model parameters such as chemical reaction rates, mixture model parameters, material property parameters, etc.
CFD↗
Main Geomagnetic Field Models from Oersted and Magsat Data Via a Rigorous General Inverse Theory with Error Bounds
The purpose of the grant was to study how prior information about the geomagnetic field can be used to interpret surface and satellite magnetic measurements, to generate quantitative descriptions of prior information that might be so used, and to use this prior information to obtain from satellite data a model of the core field with statistically justifiable error estimates. The need for prior information in geophysical inversion has long been recognized. Data sets are finite, and faithful descriptions of aspects of the earth almost always require infinite-dimensional model spaces. By themselves, the data can confine the correct earth model only to an infinite-dimensional subset of the model space. Earth properties other than direct functions of the observed data cannot be estimated from those data without prior information about the earth. Prior information is based on what the observer already knows before the data become available. Such information can be "hard" or "soft". Hard information is a belief that the real earth must lie in some known region of model space. For example, the total ohmic dissipation in the core is probably less that the total observed geothermal heat flow out of the earth's surface. (In principle, ohmic heat in the core can be recaptured to help drive the dynamo, but this effect is probably small.) "Soft" information is a probability distribution on the model space, a distribution that the observer accepts as a quantitative description of her/his beliefs about the earth. The probability distribution can be a subjective prior in the sense of Bayes or the objective result of a statistical study of previous data or relevant theories.
Backus, George E.↗
To Err is Normable: The Computation of Frequency-Domain Error Bounds from Time-Domain Data
This paper exploits the relationships among the time-domain and frequency-domain system norms to derive information useful for modeling and control design, given only the system step response data. A discussion of system and signal norms is included. The proposed procedures involve only simple numerical operations, such as the discrete approximation of derivatives and integrals, and the calculation of matrix singular values. The resulting frequency-domain and Hankel-operator norm approximations may be used to evaluate the accuracy of a given model, and to determine model corrections to decrease the modeling errors.
Hartley, Tom T.↗
Data compression with bounded errors.
Data compression from standpoint of epsilon entropy theory, considering precise measure of channel capacity necessary to describe data source
Posner, E. C.↗
Error bounds for compound quadrature of weakly singular integrals
Prediction of slow convergence rates for weakly singular numerical quadratures
Feldstein, A.↗
The dependence of WAAS ionospheric error bounds upon the spatial distribution of GPS measurements
This paper examines the dependence of the threat model upon the spatial distribution of GPS measurements.
WAAS ionosphere GPS↗
High gain feedback and telerobotic tracking
Asymptotically stable linear time invariant systems are capable of tracking arbitrary reference signals with a bounded error proportional to the magnitude of the reference signal (and its derivatives). It is shown that a similar property holds for a general class of nonlinear dynamical systems which includes all robots. As in the linear case, the error bound may be made arbitrarily small by increasing the magnitude of the feedback gains which stabilize the system.
Koditschek, D. E.↗