SEARCH · Search NASA

Results for “Optimal estimation”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 631 records · Page 35

All sources NASA NTRS DOE OSTI

NASA NTRS2013

Model-Based Control of a Nonlinear Aircraft Engine Simulation using an Optimal Tuner Kalman Filter Approach

This paper covers the development of a model-based engine control (MBEC) methodology featuring a self tuning on-board model applied to an aircraft turbofan engine simulation. Here, the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) serves as the MBEC application engine. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC over a wide range of operating points. The on-board model is a piece-wise linear model derived from CMAPSS40k and updated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. Investigations using the MBEC to provide a stall margin limit for the controller protection logic are presented that could provide benefits over a simple acceleration schedule that is currently used in traditional engine control architectures.

Control Theory↗

NASA NTRS2014

Erasing the Variable: Empirical Foreground Discovery for Global 21 cm Spectrum Experiments

Spectral measurements of the 21 cm monopole background have the promise of revealing the bulk energetic properties and ionization state of our universe from z approx. 6 − 30. Synchrotron foregrounds are orders of magnitude larger than the cosmological signal, and are the principal challenge faced by these experiments. While synchrotron radiation is thought to be spectrally smooth and described by relatively few degrees of freedom, the instrumental response to bright foregrounds may be much more complex. To deal with such complexities, we develop an approach that discovers contaminated spectral modes using spatial fluctuations of the measured data. This approach exploits the fact that foregrounds vary across the sky while the signal does not. The discovered modes are projected out of each line-of-sight of a data cube. An angular weighting then optimizes the cosmological signal amplitude estimate by giving preference to lower-noise regions. Using this method, we show that it is essential for the passband to be stable to at least approx. 10(exp −4). In contrast, the constraints on the spectral smoothness of the absolute calibration are mainly aesthetic if one is able to take advantage of spatial information. To the extent it is understood, controlling polarization to intensity leakage at the approx. 10(exp −2) level will also be essential to rejecting Faraday rotation of the polarized synchrotron emission. Subject headings: dark ages, reionization, first stars - methods: data analysis - methods: statistical

Variable↗

NASA NTRS2012

Model-Based Control of an Aircraft Engine using an Optimal Tuner Approach

This paper covers the development of a model-based engine control (MBEC) method- ology applied to an aircraft turbofan engine. Here, a linear model extracted from the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) at a cruise operating point serves as the engine and the on-board model. The on-board model is up- dated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. MBEC provides the ability for a tighter control bound of thrust over the entire life cycle of the engine that is not achievable using traditional control feedback, which uses engine pressure ratio or fan speed. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC tighter thrust control. In addition, investigations of using the MBEC to provide a surge limit for the controller limit logic are presented that could provide benefits over a simple acceleration schedule that is currently used in engine control architectures.

Connolly, Joseph W.↗

NASA NTRS2019

A Framework for Scaling in Filtering and Linear Covariance Analysis

Scaling is used extensively for numerical optimization and trajectory optimization. Its use in the estimation community is almost nonexistent. This paper creates the framework for practical scaling in space navigation, in general, and linear covariance analysis, in particular.

D'souza, Christopher N.↗

NASA NTRS2020

Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional ‘‘grid-by-grid analysis,’’ the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for ‘‘hybrid’’ data-driven and physics-driven estimates in order to make optimal usage of satellite observations.

extreme events↗

NASA NTRS2021

Near-Optimum Real-Time Range Estimation Algorithms for Proximity Links

The renewed interest in space exploration and cis-lunar situational awareness demands accurate ranging algorithms to enable navigation solutions for a multitude of spacecraft, rovers, and human explorers on the Moon and even on Mars, in the near future. Current state-of-the-art in ground-based ranging accuracy is on the order of 30 cm, however complicated equipment calibration and significant post-processing is required to achieve this level of ranging accuracy. This article examines ad-hoc approaches that achieve near-optimum real-time ranging performance with reduced complexity by utilizing a DPLL (digital phase-locked loop) to track the phase of the residual carrier for both direct and subcarrier modulated PN sequences, and a DCL (digital Costas loop) to obtain independent estimates of carrier phase and optimal combinations of these implementations to achieve near-optimum real-time performance with reduced computational complexity.

Cheung, Kar-Ming↗

NASA NTRS1966

Bending feedback control Final report

Application of quadratic optimal control, least squares state, and parameter estimation to stability and control of flexible launch vehicle

OPTIMAL CONTROL↗

NASA NTRS1985

Kalman-like estimation for static distributed systems Antenna shape from radiation measurements

This paper advances an approach to the determination of shape of static distributed systems. It also illustrates the application of the approach to the problems of surface diagnosis of large parabolic reflectors. The estimation methods developed combine in an optimal sense the information from an elliptic model of the structure and from measurements of the structural deflection and of the far-field pattern changes due to the structural deformation. The estimators have a predictor-corrector structure, quite similar to that of a Kalman filter. The system model is first used to obtain a predicted estimate. A correction term is then added to the prediction to obtain the final state estimate. The relative weighting between prediction and correction terms is determined by an estimator gain. As in a Kalman filter, the estimator gain can be expressed in terms of the state estimation error covariance.

Rodriguez, G.↗

NASA NTRS2014

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

Modal Filtering↗

NASA NTRS2016

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

Modal Filtering↗

NASA NTRS1977

Different approaches of spectral analysis

Several approaches to the problem of the calculation of spectral power density of a random function from an estimate of the autocorrelation function were studied. A comparative study was presented of these different methods. The principles on which they are based and the hypothesis implied were pointed out. Some indications on the optimization of the length of the estimated correlation function was given. An example of application of the different methods discussed in this paper was included.

Lacoume, J. L.↗

NASA NTRS1980

Research in the application of spectral data to crop identification and assessment, volume 2

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates.

Daughtry, C. S. T.↗

NASA NTRS1988

Selecting a spatial resolution for estimation of per-field green leaf area index

For any application of multispectral scanner (MSS) data, a user is faced with a number of choices concerning the characteristics of the data; one of these is their spatial resolution. A pilot study was undertaken to determine the spatial resolution that would be optimal for the per-field estimation of green leaf area index (GLAI) in grassland. By reference to empirically-derived data from three areas of grassland, the suitable spatial resolution was hypothesized to lie in the lower portion of a 2-18 m range. To estimate per-field GLAI, airborne MSS data were collected at spatial resolutions of 2 m, 5 m and 10 m. The highest accuracies of per-field GLAI estimation were achieved using MSS data with spatial resolutions of 2 m and 5 m.

Curran, Paul J.↗

NASA NTRS1998

Estimation of Aircraft Nonlinear Unsteady Parameters From Wind Tunnel Data

Aerodynamic equations were formulated for an aircraft in one-degree-of-freedom large amplitude motion about each of its body axes. The model formulation based on indicial functions separated the resulting aerodynamic forces and moments into static terms, purely rotary terms and unsteady terms. Model identification from experimental data combined stepwise regression and maximum likelihood estimation in a two-stage optimization algorithm that can identify the unsteady term and rotary term if necessary. The identification scheme was applied to oscillatory data in two examples. The model identified from experimental data fit the data well, however, some parameters were estimated with limited accuracy. The resulting model was a good predictor for oscillatory and ramp input data.

Klein, Vladislav↗

NASA NTRS2001

A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly

This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

Shen, Samuel S. P.↗

NASA NTRS2005

Revealing Relationships among Relevant Climate Variables with Information Theory

The primary objective of the NASA Earth-Sun Exploration Technology Office is to understand the observed Earth climate variability, thus enabling the determination and prediction of the climate's response to both natural and human-induced forcing. We are currently developing a suite of computational tools that will allow researchers to calculate, from data, a variety of information-theoretic quantities such as mutual information, which can be used to identify relationships among climate variables, and transfer entropy, which indicates the possibility of causal interactions. Our tools estimate these quantities along with their associated error bars, the latter of which is critical for describing the degree of uncertainty in the estimates. This work is based upon optimal binning techniques that we have developed for piecewise-constant, histogram-style models of the underlying density functions. Two useful side benefits have already been discovered. The first allows a researcher to determine whether there exist sufficient data to estimate the underlying probability density. The second permits one to determine an acceptable degree of round-off when compressing data for efficient transfer and storage. We also demonstrate how mutual information and transfer entropy can be applied so as to allow researchers not only to identify relations among climate variables, but also to characterize and quantify their possible causal interactions.

Knuth, Kevin H.↗

NASA NTRS1974

Optimal design of an unsupervised adaptive classifier with unknown priors

An adaptive detection scheme for M hypotheses was analyzed. It was assumed that the probability density function under each hypothesis was known, and that the prior probabilities of the M hypotheses were unknown and sequentially estimated. Each observation vector was classified using the current estimate of the prior probabilities. Using a set of nonlinear transformations, and applying stochastic approximation theory, an optimally converging adaptive detection and estimation scheme was designed. The optimality of the scheme lies in the fact that convergence to the true prior probabilities is ensured, and that the asymptotic error variance is minimum, for the class of nonlinear transformations considered. An expression for the asymptotic mean square error variance of the scheme was also obtained.

Kazakos, D.↗

NASA NTRS1982

Software for the grouped optimal aggregation technique

The grouped optimal aggregation technique produces minimum variance, unbiased estimates of acreage and production for countries, zones (states), or any designated collection of acreage strata. It uses yield predictions, historical acreage information, and direct acreage estimate from satellite data. The acreage strata are grouped in such a way that the ratio model over historical acreage provides a smaller variance than if the model were applied to each individual stratum. An optimal weighting matrix based on historical acreages, provides the link between incomplete direct acreage estimates and the total, current acreage estimate.

Brown, P. M.↗