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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence

A new formulation of the filter-error method for estimating aerodynamic parameters in nonlinear aircraft dynamic models during turbulence was developed and demonstrated. The approach uses an estimate of the measurement noise covariance to identify the model parameters, their uncertainties, and the process noise covariance, in a relaxation method analogous to the output-error method. Prior information on the model parameters and uncertainties can be supplied, and a post-estimation correction to the uncertainty was included to account for colored residuals not considered in the theory. No tuning parameters, needing adjustment by the analyst, are used in the estimation. The method was demonstrated in simulation using the NASA Generic Transport Model, then applied to the subscale T-2 jet-engine transport aircraft flight. Modeling results in different levels of turbulence were compared with results from time-domain output error and frequency- domain equation error methods to demonstrate the effectiveness of the approach.

Grauer, Jared A.↗
NASA NTRS1981

The MSS control point location error filter for LANDSAT-D

The filter produces the maximum likelihood estimates for average values of the spacecraft position and attitude errors during a single scene. The quality of the filter performance is characterized by the maximal cross and along track residual errors for which probability distributions can be calculated analytically for a given pattern of control points. The filter with an automatic selection of the best set of estimates provides geodetic correction at 90% of pixels with residual errors less than 40 m for four or more control points and the mean squared measurement errors of the order of 20-25 m. The same accuracy can be preserved for eight or more control points and measurement errors of 30-35 m.

Levine, I.↗
NASA NTRS1985

Identification of dynamic systems, theory and formulation

The problem of estimating parameters of dynamic systems is addressed in order to present the theoretical basis of system identification and parameter estimation in a manner that is complete and rigorous, yet understandable with minimal prerequisites. Maximum likelihood and related estimators are highlighted. The approach used requires familiarity with calculus, linear algebra, and probability, but does not require knowledge of stochastic processes or functional analysis. The treatment emphasizes unification of the various areas in estimation in dynamic systems is treated as a direct outgrowth of the static system theory. Topics covered include basic concepts and definitions; numerical optimization methods; probability; statistical estimators; estimation in static systems; stochastic processes; state estimation in dynamic systems; output error, filter error, and equation error methods of parameter estimation in dynamic systems, and the accuracy of the estimates.

Maine, R. E.↗
NASA NTRS1994

Enhanced orbit determination filter: Inclusion of ground system errors as filter parameters

The theoretical aspects of an orbit determination filter that incorporates ground-system error sources as model parameters for use in interplanetary navigation are presented in this article. This filter, which is derived from sequential filtering theory, allows a systematic treatment of errors in calibrations of transmission media, station locations, and earth orientation models associated with ground-based radio metric data, in addition to the modeling of the spacecraft dynamics. The discussion includes a mathematical description of the filter and an analytical comparison of its characteristics with more traditional filtering techniques used in this application. The analysis in this article shows that this filter has the potential to generate navigation products of substantially greater accuracy than more traditional filtering procedures.

Masters, W. C.↗
NASA NTRS1985

Filter induced errors in laser anemometry using counter-processor

Simulations of laser Doppler anemometer (LDA) systems have focused primarily on noise studies or biasing errors. Another possible source of error is the choice of filter types and filter cutoff frequencies. Before it is applied to the counter portion of the signal processor, a Doppler burst is filtered to remove the pedestal and to reduce noise in the frequency bands outside the region in which the signal occurs. Filtering, however, introduces errors into the measurement of the frequency of the input signal which leads to inaccurate results. Errors caused by signal filtering in an LDA counter-processor data acquisition system are evaluated and filters for a specific application will reduce these errors are chosen.

Oberle, L. A.↗
NASA NTRS1985

Filter induced errors in laser anemometer measurements using counter processors

Simulations of laser Doppler anemometer (LDA) systems have focused primarily on noise studies or biasing errors. Another possible source of error is the choice of filter types and filter cutoff frequencies. Before it is applied to the counter portion of the signal processor, a Doppler burst is filtered to remove the pedestal and to reduce noise in the frequency bands outside the region in which the signal occurs. Filtering, however, introduces errors into the measurement of the frequency of the input signal which leads to inaccurate results. Errors caused by signal filtering in an LDA counter-processor data acquisition system are evaluated and filters for a specific application which will reduce these errors are chosen.

Oberle, L. G.↗
NASA NTRS1978

Filtering and error analysis via the UDU super T covariance factorization

Kalman filter algorithms based on the UDU super T covariance factorization are discussed, with special attention given to algorithm implementation efficiency. A U-D-factored covariance error-analysis algorithm is formulated, and its efficiency and numerical stability are demonstrated in a representative orbit determination problem. The numerical results are compared with those obtained using covariance error-analysis formulas, and the comparison highlights the numerical superiority of the present algorithm. A byproduct of the U-D analysis is a highly efficient algorithm mechanization of the arbitrary gain covariance update formula.

Thornton, C. L.↗
NASA NTRS1986

Application of parameter estimation to highly unstable aircraft

The application of parameter estimation to highly unstable aircraft is discussed. Included are a discussion of the problems in applying the output error method to such aircraft and demonstrates that the filter error method eliminates these problems. The paper shows that the maximum likelihood estimator with no process noise does not reduce to the output error method when the system is unstable. It also proposes and demonstrates an ad hoc method that is similar in form to the filter error method, but applicable to nonlinear problems. Flight data from the X-29 forward-swept-wing demonstrator is used to illustrate the problems and methods discussed.

Maine, R. E.↗
NASA NTRS1986

Application of parameter estimation to highly unstable aircraft

This paper discusses the application of parameter estimation to highly unstable aircraft. It includes a discussion of the problems in applying the output error method to such aircraft and demonstrates that the filter error method eliminates these problems. The paper shows that the maximum likelihood estimator with no process noise does not reduce to the output error method when the system is unstable. It also proposes and demonstrates an ad hoc method that is similar in form to the filter error method, but applicable to nonlinear problems. Flight data from the X-29 forward-swept-wing demonstrator is used to illustrate the problems and methods discussed.

Maine, R. E.↗
NASA NTRS1977

Dynamic pressure studies

The effects on dynamic pressure by errors in viscous interaction of air, navigation filter errors, and wind errors were investigated. Results are summarized in tabular from.

Hall, J. W.↗
NASA NTRS2024

Aircraft Parameter Estimation Considering Process and Measurement Noise

A practical formulation is proposed for parameter estimation using the filter-error method, which is a maximum-likelihood estimator for dynamic systems having both process and measurement noise inputs. The novelty of the proposed formulation is that by accurately estimating the measurement noise covariance matrix using a time series analysis method, the remaining unknowns (which include the unknown parameters in the state-space matrices and the process noise covariance matrix) become decorrelated and can be estimated simultaneously in a straightforward manner. The approach is demonstrated using simulation data and flight test data from a subscale airplane. Results indicate that proposed algorithm can obtain accurate modeling results when both measurement noise and process noise are present in the data.

Kalman filter↗
NASA NTRS1972

Survey of digital filtering

A three part survey is made of the state-of-the-art in digital filtering. Part one presents background material including sampled data transformations and the discrete Fourier transform. Part two, digital filter theory, gives an in-depth coverage of filter categories, transfer function synthesis, quantization and other nonlinear errors, filter structures and computer aided design. Part three presents hardware mechanization techniques. Implementations by general purpose, mini-, and special-purpose computers are presented.

Nagle, H. T., Jr.↗
NASA NTRS1993

Failure modes of reduced-order orbit determination filters and their remedies

Ways in which failure can occur in reduced-order, orbit determination filter, error covariance calculations are discussed. In the context of this article, reduced-order filters denote nonoptimal filters which include fixed levels of uncertainty in some parameters of the measurement models or in the spacecraft dynamical model which are not explicitly estimated in the filter equations. Failure is defined as an increase in the orbit determination covariance with the addition of data or as an unreasonable growth in the covariance with time, i.e., nonasymptotic behavior of the covariance. Some simple, known cases of failure are discussed along with their traditional remedies. In addition, more modern remedies are discussed which are currently under development at the Jet Propulsion Laboratory. The article first describes the known problems of reduced-order filters when they are employed for orbit determination, and their traditional remedies. Then, having defined these, the relevancy and desirability of the more modern remedies are made apparent.

Scheeres, D. J.↗
NASA NTRS1969

Pulse-code-modulation baseline correction for low signal-to-noise ratios

Time-shared integrate-hold-dump circuits separate the dc level due to the signal /bit information/ from the dc signal due to the baseline error. The system detects the baseline error, filters it, and then algebraically adds enough voltage to a servo loop to reduce the error to zero.

Stephens, T. J.↗
NASA NTRS1975

Worst error performance of continuous Kalman filters

The worst error performance of estimation filters is investigated for continuous systems in this paper. The pathological performance study, without assuming any dynamical model such as Markov processes for perturbations, except for its bounded amplitude, will give practical and dependable criteria in establishing the navigation and maneuver strategy in deep space missions.

Nishimura, T.↗
NASA NTRS1979

An approach to eliminate roundoff errors in digital filters

'Second-order quantizers' are introduced which can be used for implementing recursive digital filters with practically no roundoff errors or limit-cycle oscillations. Based on the idea of changing the transfer function used to compute roundoff errors, these quantizers save the low-order bits to correct the product at future iterations. For several pole locations, this can be done with a minimal amount of extra hardware.

Abu-El-haija, A. I.↗