Search NASA⌕ Search

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.

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

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Estimation of the sugar cane cultivated area from LANDSAT images using the two phase sampling method

A two phase sampling method and the optimal sampling segment dimensions for the estimation of sugar cane cultivated area were developed. This technique employs visual interpretations of LANDSAT images and panchromatic aerial photographs considered as the ground truth. The estimates, as a mean value of 100 simulated samples, represent 99.3% of the true value with a CV of approximately 1%; the relative efficiency of the two phase design was 157% when compared with a one phase aerial photographs sample.

Parada, N. D. J.↗
NASA NTRS1989

Attitude determination and parameter estimation using vector observations - Theory

Procedures for attitude determination based on Wahba's loss function are generalized to include the estimation of parameters other than the attitude, such as sensor biases. Optimization with respect to the attitude is carried out using the q-method, which does not require an a priori estimate of the attitude. Optimization with respect to the other parameters employs an iterative approach, which does require an a priori estimate of these parameters. Conventional state estimation methods require a priori estimates of both the parameters and the attitude, while the algorithm presented in this paper always computes the exact optimal attitude for given values of the parameters. Expressions for the covariance of the attitude and parameter estimates are derived.

Markley, F. Landis↗
NASA NTRS2000

Optimal Reflectance, Transmittance, and Absorptance Wavebands and Band Ratios for the Estimation of Leaf Chlorophyll Concentration

The present study utilized regression analysis to identify: wavebands and band ratios within the 400-850 nm range that could be used to estimate total chlorophyll concentration with minimal error; and simple regression models that were most effective in estimating chlorophyll concentrations were measured for two broadleaved species, a broadleaved vine, a needle-leaved conifer, and a representative of the grass family.Overall, reflectance, transmittance, and absorptance corresponded most precisely with chlorophyll concentration at wavelengths near 700 nm, although regressions were strong as well in the 550-625 nm range.

Carter, Gregory A.↗
NASA NTRS2010

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

Simon, Donald L.↗
NASA NTRS1989

Numerically efficient algorithm for model development of high-order systems

A technique for estimating transfer functions in partial fraction expansion form from frequency response data for a high-order system is presented. The problem formulation avoids many of the numerical difficulties associated with high-order polynomials and has the advantage of having the option to fix the camping and frequency of a mode, if known, during the estimation process. The resulting transfer function(s) may be converted to Jordan-Form time domain equations directly. During the implementation of this technique, a frequency and amplitude normalizing window was developed that maximized the efficiency of the optimization algorithm. The combination of estimating the transfer function in factored form, the ability to fix preciously determined parameters and the effectiveness of the normalizing window led to a progressive approach to synthesizing transfer functions from frequency response data for high-order systems.

Parada, L. O.↗
NASA NTRS1980

Implicit model following and parameter identification of unstable aircraft

A transformation in the s-plane is described which has utility in implicit model-following optimal control design application and in estimation or parameter identification problems. The objective of the transformation is, for the control problem, to achieve an unstable closed-loop system, and, for the estimation problem, to alleviate algorithm convergence problems that may arise in identifying unstable systems. For the control problem, the transformation is a shift along the real (sigma) axis of the plant and model poles and zeros. This transformation is shown to be equivalent to a modified performance index but offers the advantage of compatibility with existing optimal control solution algorithms. For the estimation problem, the data are multiplied by an exponential function and the assumed measurement and process noise covariances are appropriately modified. Examples of both control and estimation applications are presented.

Lebacqz, J. V.↗
NASA NTRS1988

Hydrogen turbines for space power systems: A simplified axial flow gas turbine model

Hydrogen cooled, turbine powered space weapon systems require a relatively simple, but reasonably accurate hydrogen gas expansion turbine model. Such a simplified turbine model would require little computational time and allow incorporation into system level computer programs while providing reasonably accurate volume/mass estimates. This model would then allow optimization studies to be performed on multiparameter space power systems and provide improved turbine mass and size estimates for the various operating conditions (when compared to empirical and power law approaches). An axial flow gas expansion turbine model was developed for these reasons and is in use as a comparative bench mark in space power system studies at Sandia. The turbine model is based on fluid dynamic, thermodynamic, and material strength considerations, but is considered simplified because it does not account for design details such as boundary layer effects, shock waves, turbulence, stress concentrations, and seal leakage. Although the basic principles presented here apply to any gas or vapor axial flow turbine, hydrogen turbines are discussed because of their immense importance on space burst power platforms.

Hudson, Steven L.↗
NASA NTRS1980

Wavelength predictions for lines of interest in the Ne I sequence

It is noted that the resonance lines of ions in the neon isoelectric sequence are prominent at short wavelengths. Recent observations and calculations in the literature are used to derive estimated wavelengths of likely observable lines emitted by neonlike ions. Optimal conditions for observation are briefly discussed, and estimated wavelengths for three transitions of the neon isoelectric sequence are presented.

Kastner, S. O.↗
NASA NTRS1990

Parameter learning for performance adaptation

A parameter learning method is introduced and used to broaden the region of operability of the adaptive control system of a flexible space antenna. The learning system guides the selection of control parameters in a process leading to optimal system performance. A grid search procedure is used to estimate an initial set of parameter values. The optimization search procedure uses a variation of the Hooke and Jeeves multidimensional search algorithm. The method is applicable to any system where performance depends on a number of adjustable parameters. A mathematical model is not necessary, as the learning system can be used whenever the performance can be measured via simulation or experiment. The results of two experiments, the transient regulation and the command following experiment, are presented.

Peek, Mark D.↗
NASA NTRS2016

Model-Based Engine Control Architecture with an Extended Kalman Filter

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

engine control↗
NASA NTRS2016

Model-Based Engine Control Architecture with an Extended Kalman Filter

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The non-linear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

engine control↗
NASA NTRS1991

Optimal control of systems with capacity: Related noises

In the ordinary theory of optimal control (LQR and Kalman filter), the variances of the actuators and the sensors are assumed to be known (not related to the capacities of the devices). This assumption is not true in practice. Generally, a device with greater capacity to exert actuating forces and a sensor capable of sensing greater sensing range will generate noise of greater power spectral density. When the ordinary theory of optimal control is used to estimate the errors of the outputs in such cases it will lead to faulty results, because the capacities of such devices are unknown before the system is designed. The performance of the system designed by the ordinary theory will not be optimal as the variances of the sensors and the actuators are neither known nor constant. The interaction between the control system and structure could be serious because the ordinary method will lead to greater feedback (Kalman gain) matrices. Methods which can optimize the performance of systems when noises of the actuators and the sensors are related to their capacities are developed. These methods will result in smaller feedback (Kalman gain) matrix.

Ruan, Milfang↗
NASA NTRS2009

Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Model Performance in a Real-time Air Quality Estimation System

Aerosol optical depth (AOD), an indirect estimate of particle matter using satellite observations, has shown great promise in improving estimates of PM 2.5 air quality surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM 2.5 surface estimation in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) the approach to integrate satellite measurements with ground measurements in the pollution estimation, and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect on the identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale of within the last 30 days displays the best model performance in this study area using 2004 and 2005 data sets.

Li, Hui↗
NASA NTRS1992

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust.

Orme, John S.↗
NASA NTRSDate not supplied

Improved Assessment of Recent Trends in NOx and VOC Emissions and Ozone Production Sensitivity Regimes Using Satellite Data

This presentation highlights results from a NASA Aura Science Team and Atmospheric Composition Modeling and Analysis Program (ACMAP) project which study the capability to observe and model trends in ozone (O3) production regimes using spaceborne sensors. Ultraviolet– visible (UV–Vis) tropospheric column satellite retrievals of formaldehyde (HCHO) (a proxy for volatile organic compound [VOC] reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides [NOx]) are frequently used to investigate the sensitivity of O3 production to emissions of NOx and VOCs. There are challenges that come from using satellite-derived ratios of HCHO and NO2 (FNR) to study O3 production sensitivity with the largest uncertainties associated with specific spaceborne sensor’s retrieval biases and errors. This study quantifies the differences and improvements in satellite retrievals of O3 production sensitivity regimes using FNRs when moving from legacy polar orbiting satellites such as the Ozone Monitoring Instrument (OMI) onboard NASA’s Aura satellite and Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) onboard the NASA/NOAA Suomi-NPP platform to newer, higher spatiotemporal resolution satellite sensors TROPOspheric Monitoring Instrument (TROPOMI) and eventually the recently launched NASA geostationary sensor Tropospheric Emissions: Monitoring of Pollution (TEMPO). Furthermore, we investigate how using retrievals of NO2 and HCHO from these different satellites to constrain model predictions impacts the ability to accurately simulate O3 chemistry including chemical production regimes. To this end, we have conducted inverse model simulations, using the WRF-CMAQ-DDM data assimilation system at 12 km × 12 km, to constrain emissions of NOx and VOCs over the contiguous United States (CONUS) when assimilating OMI and TROPOMI retrievals of NO2 and HCHO. Two advantages of this are that we a) account for each satellite’s errors/biases in the emission estimation and b) update the prior profile to ensure that only radiance information is used for optimizing the emissions. This presentation will demonstrate: a) the varying accuracy of different satellite retrieved FNRs and ability to capture known sub-annual emission trends (e.g., seasonal, weekend/weekday) and emission anomalies during the COVID-19 lockdown of 2020, b) the differences and improvements in top-down emission estimates of NOx and VOCs when constrained by newer satellite sensors compared to legacy systems, and c) multi-sensor optimized emission estimates of summer-time NOx and VOCs between 2019-2021.

Data↗
NASA NTRS1991

On the estimation of climatological Z-R relationships

A statistical framework for climatological Z-R parameter estimation is developed and simulation experiments are conducted to examine sampling properties of the estimators. Both parametric and nonparametric models are considered. For parametric models, it is shown that Z-R parameters can be estimated by maximum likelihood, a procedure with optimal large sample properties. A general nonparametric framework for climatological Z-R estimation is also developed. Nonparametric procedures are attractive because of their flexibility in dealing with certain types of measurement errors common to radar data. Simulation experiments show that even under favorable assumptions on error characteristics of radar and raingages, large datasets are required to obtain accurate Z-R parameter estimates. Another important conclusion is that estimation results are generally quite sensitive to radar and raingage measurement thresholds. For fixed sample size, the simulation results can be used to provide quantitative assessments of the accuracy of Z-R model parameter estimates. These results are particularly useful for error analysis of precipitation products that are derived using climatological Z-R relations. One example is the large-area rainfall estimates derived using the height-area rainfall threshold (HART) technique.

Krajewski, Witold F.↗
NASA NTRS1976

Applications of vortex lattice theory to preliminary aerodynamic design

Some applications of the vortex-lattice theory to the preliminary aerodynamic design and analysis of subsonic aircraft were presented. These methods include the Rockwell-Tulinius vortex-lattice theory for estimating aerodynamic characteristics, a Trefftz plane optimization procedure for determining the span loads for minimum induced drag, and a modification of the Trefftz plane procedure to estimate the induced drag for specified span loads. The first two methods are used to aerodynamically design aircraft planforms, twists, and cambers, and the latter method is used to estimate the drag for components such as flaps and control surfaces. Results from the theories for predicting lift and pitching moment, drag due to lift, and the drag of control surfaces are compared with experimental data. This data was obtained on a general aviation model with flaps and a close-coupled canard-wing model.

Paulson, J. W., Jr.↗