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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.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

On-line methods for rotorcraft aeroelastic mode identification

The requirements for the on-line identification of rotorcraft aeroelastic blade modes from random response test data are presented. A recursive maximum likelihood (RML) technique is used in conjunction with a bandpass filter to identify isolated blade mode damping and frequency. The RML technique is demonstrated to have excellent convergence characteristics in random measurement noise and random process noise excitation. The RML identification technique uses an ARMA representation for the aeroelastic stochastic system and requires virtually no user interaction while providing accurate confidence bands on the parameter estimates. Comparisons are made with an off-line Newton type maximum likelihood algorithm which uses a state variable model representation. Results are presented from simulation random response data which quantify the identifed parameter convergence behavior for various levels of random excitation which is typical of wind tunnel turbulence levels. The RML technique is applied to hingless rotor test data from the NASA Langley Research Center Helicopter Hover Facility.

Molusis, J. A.↗
NASA NTRS1988

Determination of GPS orbits to submeter accuracy

Orbits for satellites of the Global Positioning System (GPS) were determined with submeter accuracy. Tests used to assess orbital accuracy include orbit comparisons from independent data sets, orbit prediction, ground baseline determination, and formal errors. One satellite tracked 8 hours each day shows rms error below 1 m even when predicted more than 3 days outside of a 1-week data arc. Differential tracking of the GPS satellites in high Earth orbit provides a powerful relative positioning capability, even when a relatively small continental U.S. fiducial tracking network is used with less than one-third of the full GPS constellation. To demonstrate this capability, baselines of up to 2000 km in North America were also determined with the GPS orbits. The 2000 km baselines show rms daily repeatability of 0.3 to 2 parts in 10 to the 8th power and agree with very long base interferometry (VLBI) solutions at the level of 1.5 parts in 10 to the 8th power. This GPS demonstration provides an opportunity to test different techniques for high-accuracy orbit determination for high Earth orbiters. The best GPS orbit strategies included data arcs of at least 1 week, process noise models for tropospheric fluctuations, estimation of GPS solar pressure coefficients, and combine processing of GPS carrier phase and pseudorange data. For data arc of 2 weeks, constrained process noise models for GPS dynamic parameters significantly improved the situation.

Bertiger, W. I.↗
NASA NTRS1980

Determination of instrumentation errors from measured data using maximum likelihood method

The maximum likelihood method is used for estimation of unknown initial conditions, constant bias and scale factor errors in measured flight data. The model for the system to be identified consists of the airplane six-degree-of-freedom kinematic equations, and the output equations specifying the measured variables. The estimation problem is formulated in a general way and then, for practical use, simplified by ignoring the effect of process noise. The algorithm developed is first applied to computer generated data having different levels of process noise for the demonstration of the robustness of the method. Then the real flight data are analyzed and the results compared with those obtained by the extended Kalman filter algorithm.

Keskar, D. A.↗
NASA NTRS2004

Spacecraft methods and structures with enhanced attitude control that facilitates gyroscope substitutions

Methods and structures are provided that enhance attitude control during gyroscope substitutions by insuring that a spacecraft's attitude control system does not drive its absolute-attitude sensors out of their capture ranges. In a method embodiment, an operational process-noise covariance Q of a Kalman filter is temporarily replaced with a substantially greater interim process-noise covariance Q. This replacement increases the weight given to the most recent attitude measurements and hastens the reduction of attitude errors and gyroscope bias errors. The error effect of the substituted gyroscopes is reduced and the absolute-attitude sensors are not driven out of their capture range. In another method embodiment, this replacement is preceded by the temporary replacement of an operational measurement-noise variance R with a substantially larger interim measurement-noise variance R to reduce transients during the gyroscope substitutions.

Li, Rongsheng↗
DOE OSTI2020

Sequential Bayesian Parameter Estimation of Stochastic Dynamic Load Models

In this paper we focus on the parameter estimation of dynamic load models with stochastic terms-in particular, load models where protection settings are uncertain, such as in aggregated air conditioning units. We show how the uncertainty in the aggregated protection characteristics can be formulated as a stochastic differential equation with process noise. We cast the parameter inversion within a Bayesian parameter estimation framework, and we present methods to include process noise. We demonstrate the benefits of considering stochasticity in the parameter estimation and the risks of ignoring it.

Bayesian Statistics↗
NASA NTRSDate not supplied

EARLY INFORMATION PARAMETER-SET ANALYSIS FOR SATELLITE CLOSE APPROACHES USING MACHINE LEARNING

In spaceflight navigation applications, understanding and accurately applying orbital mechanics by leveraging force models for trajectory predictions will always remain an important aspect in space mission design and operations. In the process of capturing the dynamics and perturbations in the space environment, the force models are not all encompassing in that these models are subject to errors, commonly referred to as process noise. Therefore, in predicting state vectors of space objects such as spacecraft or debris over long periods of time, these errors in the process noise tend to grow over time.

machine learning↗
NASA NTRS1974

Parameter estimation using an a posteriori criterion

A procedure for estimating noise statistics as well as system model parameters, based on a maximum likelihood criterion, has been successfully applied to a number of aircraft parameter-identification problems. In the present investigation, an a posteriori criterion is examined for use in such an application, when it is desired to model process noise. The criteria lead to identical results when there is no process noise.

Bach, R. E., Jr.↗
NASA NTRS2012

Linear Covariance Analysis and Epoch State Estimators

This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.

Markley, F. Landis↗
NASA NTRS2014

Linear Covariance Analysis and Epoch State Estimators

This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.

Linear covariance analysis↗
NASA NTRS1994

Long-term persistence of solar activity

The solar irradiance has been found to change by 0.1% over the recent solar cycle. A change of irradiance of about 0.5% is required to effect the Earth's climate. How frequently can a variation of this size be expected? We examine the question of the persistence of non-periodic variations in solar activity. The Huerst exponent, which characterizes the persistence of a time series (Mandelbrot and Wallis, 1969), is evaluated for the series of C-14 data for the time interval from about 6000 BC to 1950 AD (Stuiver and Pearson, 1986). We find a constant Huerst exponent, suggesting that solar activity in the frequency range of from 100 to 3000 years includes an important continuum component in addition to the well-known periodic variations. The value we calculate, H approximately equal to 0.8, is significantly larger than the value of 0.5 that would correspond to variations produced by a white-noise process. This value is in good agreement with the results for the monthly sunspot data reported elsewhere, indicating that the physics that produces the continuum is a correlated random process (Ruzmaikin et al., 1992), and that is is the same type of process over a wide range of time interval lengths. We conclude that the time period over which an irradiance change of 0.5% can be expected to occur is significantly shorter than that which would be expected for variations produced by a white-noise process.

Ruzmaikin, Alexander↗
NASA NTRS1988

Optimal post-experiment estimation of poorly modeled dynamic systems

Recently, a novel strategy for post-experiment state estimation of discretely-measured dynamic systems has been developed. The method accounts for errors in the system dynamic model equations in a more general and rigorous manner than do filter-smoother algorithms. The dynamic model error terms do not require the usual process noise assumptions of zero-mean, symmetrically distributed random disturbances. Instead, the model error terms require no prior assumptions other than piecewise continuity. The resulting state estimates are more accurate than filters for applications in which the dynamic model error clearly violates the typical process noise assumptions, and the available measurements are sparse and/or noisy. Estimates of the dynamic model error, in addition to the states, are obtained as part of the solution of a two-point boundary value problem, and may be exploited for numerous reasons. In this paper, the basic technique is explained, and several example applications are given. Included among the examples are both state estimation and exploitation of the model error estimates.

Mook, D. Joseph↗
NASA NTRS1973

Orbit determination for low-thrust spacecraft: Concepts and analysis

Earth-based orbit determination capability for SEP spacecraft in multistation tracking and in thrust subsystem error modeling is described. Five different tracking strategies are applied to a 15 day segment of an Encke rendezvous mission. Both optimal and suboptimal orbit determination performance are determined for a wide range of process noise parameter values. The multi-station tracking techniques are found to be extremely effective, reducing orbit determination errors by orders of magnitude over that obtained with conventional single-station tracking. Explicitly differenced multistation data (QVLBI) is found to be least sensitive to gross modeling errors, but if a reasonably good process noise model is available, explicit differencing is not required.

Mcdanell, J. P.↗
NASA NTRS1990

Estimation and filtering for high-precision GPS positioning applications

Estimation of GPS satellite orbits and other parameters for high-accuracy geodetic and tracking applications is carried out with a multi-satellite batch sequential pseudo-epoch state process noise filter. Both square-root information filtering and UD factorized covariance filtering and smoothing formulations are implemented in the software to enhance numerical stability. The filtering algorithms have been arranged to take advantage of sparse matrices and other characteristics of the GPS measurement scenarios. The filter includes unique error evaluation capabilities to assess effects from mismodeling. Process noise plays a key role in the orbit determination for stochastic behavior of transmitter/receiver clocks, atmospheric-induced delay fluctuations, and unmodeled spacecraft accelerations. Present GPS orbit accuracy is better than 1 m, with ground baselines determined simultaneously to relative accuracy of 1.5 parts in 10 to the 8th over several thousand km distance.

Lichten, Stephen M.↗
NASA NTRS1990

Attitude determination error analysis - General model and specific application

This paper presents a comprehensive approach to filter and dynamics modeling for attitude determination error analysis. The discussion includes models for both batch least-squares and sequential estimators, a specific dynamic model for attitude determination error analysis of a three-axis stabilized spacecraft equipped with strapdown gyros, and the incorporation of general attitude sensor observations. An analyst using this approach to perform an error analysis chooses a subset of the spacecraft parameters to be 'solve-for' parameters, which are to be estimated, and another subset to be 'consider' parameters, which are assumed to have errors but not to be estimated. The result of the error analysis is an indication of overall uncertainties in the 'solve-for' parameters, as well as the contributions of the various error sources to these uncertainties, including those of errors in the a priori 'solve-for' estimates, of measurement noise, of dynamic noise (also known as process noise or plant noise), and of 'consider' parameter uncertainties. The analysis of attitude, star tracker alignment, and gyro bias uncertainties for the Gamma Ray Observatory spacecraft provide a specific example of the use of a general-purpose software package incorporating these models.

Markley, F. Landis↗
NASA NTRS2000

Filtering in Hybrid Dynamic Bayesian Networks

We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore, we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations of these. In the watertank simulation, we also demonstrate the use of 'non-strict' Rao-Blackwellisation. We show that the unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement noise level that the process noise level. Theory and implementation is based on (v.d. Merwe et al., 2000).

Andersen, Morten Nonboe↗
NASA NTRS1993

Neuromorphic Learning From Noisy Data

Two reports present numerical study of performance of feedforward neural network trained by back-propagation algorithm in learning continuous-valued mappings from data corrupted by noise. Two types of noise considered: plant noise which affects dynamics of controlled process and data-processing noise, which occurs during analog processing and digital sampling of signals. Study performed with view toward use of neural networks as neurocontrollers to substitute for, or enhance, performances of human experts in controlling mechanical devices in presence of sensor and actuator noise and to enhance performances of more-conventional digital feedback electronic process controllers in noisy environments.

Merrill, Walter C.↗
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↗