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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Real time thermal imaging of high temperature semiconductor melts

A real time thermal imaging system with temperature resolution better than + or - 1 C and spatial resolution of better than 0.5 mm was developed and applied to the analysis of melt surface thermal field distributions in both Czochralski and liquid encapsulated Czochralski (LEC) growth configurations. The melt is viewed in near normal incidence by a high resolution charge coupled device camera to which is attached a very narrow bandpass filter. The resulting image is digitized and processed using a pipelined pixel processor operating at an effective 40 million operations per second thus permitting real time high frequency spatial and temporal filtering of the high temperature scene. A multi-pixel averaging algorithm was developed which permits localized, low noise sensing of temperature variations at any location in the hot zone as a function of time. This signial is used to implement initial elements of a feedforward growth control scheme which is aimed at reducing disturbances to the melt caused by the batch nature of the growth process. The effect of magnetic melt stabilization on radial melt temperature distributions was measured using this technique. Problems associated with residual internal reflections and non-optimized path geometry are discussed.

Wargo, Michael J.↗
NASA NTRS1989

Control of sound radiation from a wavepacket over a curved surface

Active control of acoustic pressure in the far field resulting from the growth and decay of a wavepacket convecting in a boundary layer over a concave-convex surface is investigated numerically using direct computations of the Navier-Stokes equations. The resulting sound radiation is computed using linearized Euler equations with the pressure from the Navier-Stokes solution as a time-dependent boundary condition. The acoustic far field exhibits directivity type of behavior that points upstream to the flow direction. A fixed control algorithm is used where the attenuation signal is synthesized by a filter which actively adapt it to the amplitude-time response of the outgoing acoustic wave.

Maestrello, Lucio↗
NASA NTRS1991

Small convolution kernels for high-fidelity image restoration

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

Reichenbach, Stephen E.↗
NASA NTRS1994

Groundspeed filtering for CTAS

Ground speed is one of the radar observables which is obtained along with position and heading from NASA Ames Center radar. Within the Center TRACON Automation System (CTAS), groundspeed is converted into airspeed using the wind speeds which CTAS obtains from the NOAA weather grid. This airspeed is then used in the trajectory synthesis logic which computes the trajectory for each individual aircraft. The time history of the typical radar groundspeed data is generally quite noisy, with high frequency variations on the order of five knots, and occasional 'outliers' which can be significantly different from the probable true speed. To try to smooth out these speeds and make the ETA estimate less erratic, filtering of the ground speed is done within CTAS. In its base form, the CTAS filter is a 'moving average' filter which averages the last ten radar values. In addition, there is separate logic to detect and correct for 'outliers', and acceleration logic which limits the groundspeed change in adjacent time samples. As will be shown, these additional modifications do cause significant changes in the actual groundspeed filter output. The conclusion is that the current ground speed filter logic is unable to track accurately the speed variations observed on many aircraft. The Kalman filter logic however, appears to be an improvement to the current algorithm used to smooth ground speed variations, while being simpler and more efficient to implement. Additional logic which can test for true 'outliers' can easily be added by looking at the difference in the a priori and post priori Kalman estimates, and not updating if the difference in these quantities is too large.

Slater, Gary L.↗
NASA NTRS1997

Experimental Characterization of Hysteresis in a Revolute Joint for Precision Deployable Structures

Recent studies of the micro-dynamic behavior of a deployable telescope metering truss have identified instabilities in the equilibrium shape of the truss in response to low-energy dynamic loading. Analyses indicate that these micro-dynamic instabilities arise from stick-slip friction within the truss joints (e.g., hinges and latches). The present study characterizes the low-magnitude quasi-static load cycle response of the precision revolute joints incorporated in the deployable telescope metering truss, and specifically, the hysteretic response of these joints caused by stick-slip friction within the joint. Detailed descriptions are presented of the test setup and data reduction algorithms, including discussions of data-error sources and data-filtering techniques. Test results are presented from thirteen specimens, and the effects of joint preload and manufacturing tolerances are investigated. Using a simplified model of stick-slip friction, a relationship is made between joint load-cycle behavior and micro-dynamic dimensional instabilities in the deployable telescope metering truss.

Lake, Mark S.↗
NASA NTRS2014

Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputoutput structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross-correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moistureNDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities andor complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

soil moisture↗
NASA NTRS2019

Detection of Residual “Hot Spots” in RFI-Filtered SMAP Data

Radio frequency interference (RFI) is a well-documented problem for passive remote sensing of the Earth at L-band even though the measurements are made in the protected band at 1.413 GHz. Consequently, filtering for RFI is an important early step in the processing of measurements made by the SMAP (Soil Moisture Active/Passive) radiometer. However, the filtered data still include regions with suspiciously high antenna temperatures. One possible cause of these “hot spots” is interference not fully detected during RFI filtering. This paper presents evidence supporting this hypothesis and describes an algorithm to identify these “hot spots” so that they can be removed from the measurements. The impact of removing these “hot spots” is generally small, but evidence is presented that the brightness temperature and soil moisture improve when the hot spots are removed.

Soldo, Yan↗
NASA NTRS2012

A review of reduced Kalman filters for clock ensembles

This paper reviews the author’s previous work on free-running timescales based on Kalman filters that act upon clock comparisons. The natural Kalman clock ensemble algorithm tends to optimize long-term timescale stability at the expense of short-term stability. By subjecting each postmeasurement error covariance matrix to a non-transparent reduction operation, one obtains corrected clocks with improved short-term stability and little sacrifice of long-term stability. A new result on covariance matrix reduction is also stated.

Greenhall, Charles A.↗
NASA NTRS1997

Designing Dielectric Grating Filters with PGAPACK

The chapter is concerned with the design of planar dielectric layer diffraction gratings, which exhibit sharp resonances due to the coupling of exterior evanescent diffractive fields to the leaky modes of dielectric waveguides.

genetic algorithms↗
NASA NTRS1997

Accurate Magnetometer/Gyroscope Attitudes Using a Filter with Correlated Sensor Noise

Magnetometers and gyroscopes have been shown to provide very accurate attitudes for a variety of spacecraft. These results have been obtained, however, using a batch-least-squares algorithm and long periods of data. For use in onboard applications, attitudes are best determined using sequential estimators such as the Kalman filter. When a filter is used to determine attitudes using magnetometer and gyroscope data for input, the resulting accuracy is limited by both the sensor accuracies and errors inherent in the Earth magnetic field model. The Kalman filter accounts for the random component by modeling the magnetometer and gyroscope errors as white noise processes. However, even when these tuning parameters are physically realistic, the rate biases (included in the state vector) have been found to show systematic oscillations. These are attributed to the field model errors. If the gyroscope noise is sufficiently small, the tuned filter 'memory' will be long compared to the orbital period. In this case, the variations in the rate bias induced by field model errors are substantially reduced. Mistuning the filter to have a short memory time leads to strongly oscillating rate biases and increased attitude errors. To reduce the effect of the magnetic field model errors, these errors are estimated within the filter and used to correct the reference model. An exponentially-correlated noise model is used to represent the filter estimate of the systematic error. Results from several test cases using in-flight data from the Compton Gamma Ray Observatory are presented. These tests emphasize magnetometer errors, but the method is generally applicable to any sensor subject to a combination of random and systematic noise.

Sedlak, J.↗
NASA NTRS2007

Mapped Landmark Algorithm for Precision Landing

A report discusses a computer vision algorithm for position estimation to enable precision landing during planetary descent. The Descent Image Motion Estimation System for the Mars Exploration Rovers has been used as a starting point for creating code for precision, terrain-relative navigation during planetary landing. The algorithm is designed to be general because it handles images taken at different scales and resolutions relative to the map, and can produce mapped landmark matches for any planetary terrain of sufficient texture. These matches provide a measurement of horizontal position relative to a known landing site specified on the surface map. Multiple mapped landmarks generated per image allow for automatic detection and elimination of bad matches. Attitude and position can be generated from each image; this image-based attitude measurement can be used by the onboard navigation filter to improve the attitude estimate, which will improve the position estimates. The algorithm uses normalized correlation of grayscale images, producing precise, sub-pixel images. The algorithm has been broken into two sub-algorithms: (1) FFT Map Matching (see figure), which matches a single large template by correlation in the frequency domain, and (2) Mapped Landmark Refinement, which matches many small templates by correlation in the spatial domain. Each relies on feature selection, the homography transform, and 3D image correlation. The algorithm is implemented in C++ and is rated at Technology Readiness Level (TRL) 4.

Johnson, Andrew↗
NASA NTRS1983

Large array VLSI filter

A 35 by 35 element pipelined convolutional kernel is being fabricated using VLSI chips, each containing a 5 by 1 segment of the kernel. Three levels of printed circuitry are used: the first level is used for the VLSI chips, the second level connects seven chips together on one platform, and the third level connects seven platforms with associated delay lines, all fitting on one board. Therefore, on each board there are seven rows of the kernel containing 245 multipliers and adders, and five such boards complete the kernel array. Each multiplier accepts an 8 bit picture element which is multiplied by a 16 bit weight. A truncated 22 bit product is added to a previously stored product sum and the results are shifted to the following multiplier as the next picture element is read in. The multiplier uses a modified Booth algorithm to reduce the number of shift add operations nearly in half. The filter box is presently configured as an ancillary box to a VAX 11/780, but can be connected to essentially any CPU. The I/O bandwidth is easily compatible with most CPU devices.

Nathan, R.↗
NASA NTRS1990

Kalman filter based range estimation for autonomous navigation using imaging sensors

Rotorcraft operating in high-threat environments fly close to the surface of the earth to utilize surrounding terrain, vegetation, or man-made objects to minimize the risk of being detected by the enemy. Two basic requirements for obstacle avoidance are detection and range estimation of the object from the current rotorcraft position. There are many approaches to the estimation of range using a sequence of images. The approach used in this analysis differes from previous methods in two significant ways: an attempt is not made to estimate the rotorcraft's motion from the images; and the interest lies in recursive algorithms. The rotorcraft parameters are assumed to be computed using an onboard inertial navigation system. Given a sequence of images, using image-object differential equations, a Kalman filter (Sridhar and Phatak, 1988) can be used to estimate both the relative coordinates and the earth coordinates of the objects on the ground. The Kalman filter can also be used in a predictive mode to track features in the images, leading to a significant reduction of search effort in the feature extraction step of the algorithm. The purpose is to summarize early results obtained in extending the Kalman filter for use with actual image sequences. The experience gained from the application of this algorithm to real images is very valuable and is a necessary step before proceeding to the estimation of range during low-altitude curvilinear flight. A simple recursive method is presented to estimate range to objects using a sequence of images. The method produces good range estimates using real images in a laboratory set up and needs to be evaluated further using several different image sequences to test its robustness. The feature generation part of the algorithm requires further refinement on the strategies to limit the number of features (Sridhar and Phatak, 1989). The extension of the work reported here to curvilinear flight may require the use of the extended Kalman filter.

Sridhar, Banavar↗
NASA NTRS1994

Maximum-likelihood spectral estimation and adaptive filtering techniques with application to airborne Doppler weather radar

This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.

Lai, Jonathan Y.↗
DOE OSTI2021

Efficient phase-factor evaluation in quantum signal processing

Quantum signal processing (QSP) is a powerful quantum algorithm to exactly implement matrix polynomials on quantum computers. Asymptotic analysis of quantum algorithms based on QSP has shown that asymptotically optimal results can in principle be obtained for a range of tasks, such as Hamiltonian simulation and the quantum linear system problem. A further benefit of QSP is that it uses a minimal number of ancilla qubits, which facilitates its implementation on near-to-intermediate term quantum architectures. However, there is so far no classically stable algorithm allowing computation of the phase factors that are needed to build QSP circuits. Existing methods require the use of variable precision arithmetic and can only be applied to polynomials of a relatively low degree. We present here an optimization-based method that can accurately compute the phase factors using standard double precision arithmetic operations. We demonstrate the performance of this approach with applications to Hamiltonian simulation, eigenvalue filtering, and quantum linear system problems. Furthermore, our numerical results show that the optimization algorithm can find phase factors to accurately approximate polynomials of a degree larger than 10000 with errors below 10 -12 .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1987

Optimum filters and smoothers design for carrier phase and frequency tracking

The report presents the application of fixed lag smoothing algorithms to the problem of estimation of the phase and frequency of a sinusoidal carrier received in the presence of process noise and additive observation noise. A suboptimal structure consists of a phase-locked loop (PLL) followed by post-loop correction to the phase and frequency estimates. When the PLL is operating under high signal-to-noise ratio, the phase detector is approximately linear, and the smoother equations then correspond to the optimal linear equations for an equivalent linear signal model. The performance of such a smoother can be predicted by linear filtering theory. However, if the PLL is operating near the threshold region of the signal-to-noise ratio, the phase detector cannot be assumed to be linear. Then the actual performance of the smoother can significantly differ from that predicted by linear theory. In this report we present both the theoretical and simulated performance of such smoothers derived on the basis of various models for the phase and frequency processes.

Kumar, Rajendra↗
NASA NTRS2006

Aeroelastic Flight Data Analysis with the Hilbert-Huang Algorithm

This report investigates the utility of the Hilbert Huang transform for the analysis of aeroelastic flight data. It is well known that the classical Hilbert transform can be used for time-frequency analysis of functions or signals. Unfortunately, the Hilbert transform can only be effectively applied to an extremely small class of signals, namely those that are characterized by a single frequency component at any instant in time. The recently-developed Hilbert Huang algorithm addresses the limitations of the classical Hilbert transform through a process known as empirical mode decomposition. Using this approach, the data is filtered into a series of intrinsic mode functions, each of which admits a well-behaved Hilbert transform. In this manner, the Hilbert Huang algorithm affords time-frequency analysis of a large class of signals. This powerful tool has been applied in the analysis of scientific data, structural system identification, mechanical system fault detection, and even image processing. The purpose of this report is to demonstrate the potential applications of the Hilbert Huang algorithm for the analysis of aeroelastic systems, with improvements such as localized online processing. Applications for correlations between system input and output, and amongst output sensors, are discussed to characterize the time-varying amplitude and frequency correlations present in the various components of multiple data channels. Online stability analyses and modal identification are also presented. Examples are given using aeroelastic test data from the F-18 Active Aeroelastic Wing airplane, an Aerostructures Test Wing, and pitch plunge simulation.

Brenner, Martin J.↗
NASA NTRS1990

A preliminary evaluation of an F100 engine parameter estimation process using flight data

The parameter estimation algorithm developed for the F100 engine is described. The algorithm is a two-step process. The first step consists of a Kalman filter estimation of five deterioration parameters, which model the off-nominal behavior of the engine during flight. The second step is based on a simplified steady-state model of the 'compact engine model' (CEM). In this step the control vector in the CEM is augmented by the deterioration parameters estimated in the first step. The results of an evaluation made using flight data from the F-15 aircraft are presented, indicating that the algorithm can provide reasonable estimates of engine variables for an advanced propulsion-control-law development.

Maine, Trindel A.↗