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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 73 records · Page 4

An optimal recursive filter for the attitude determination of the Spacelab instrument pointing subsystem

The instrument pointing subsystem (IPS) is a three-axis stabilized platform developed to point various astronomical observation instruments aboard NASA's Shuttle. In order to compensate for system drifts and attitude errors, the IPS utilizes a linearized Kalman filter (LKF) with precomputed gains. Since the a priori gains are generated for an expected system configuration, any system variations result in a suboptimal estimation process. The performance of two real-time estimation filters is compared with that of the current LKF implementation. It is shown that a real-time estimation filter that reduces sensitivity to system variations and preflight computations is feasible.

West, Mark E.↗

Estimation of Timing Performance with Pulsed Laser Links for Small Satellite PNT

Satellite constellations are growing in size and in utility for applications as diverse as providing reliable, low-latency internet service to rural areas orEarth science missions.This expansion of satellite constellations brings attention to the need for responsive and reliable satellite communication.Current research in the PrecisionSpace Systems Laboratory at the University of Florida focuses on using pulsed laser communications as the method of relaying time signals instead of the radio frequency (RF)signals.The upcoming CLICK (CubeSat Laser Infrared CrosslinK) missions (Massachusetts Institute of Technology, University of Florida, NASA Ames Research Center) will demonstrate a spacecraft-to-spacecraft laser communication link and time-transfer.For pre-flight technology assessment of missions such as CLICK, a simulation of a spacecraft constellation in Earth orbit has been created to estimate clock synchronization and precision orbit determination based on measured instrumentation performance.We have developed a novel MATLAB-based numerical simulator to model spacecraft-to-spacecraft laser time-transfer and estimate the offset between the spacecraft clocks over time.This simulation includes timing errors associated with laser pulse generation and detection, as well as non-Gaussian clock drift models.The two on-board clocks modeled are a cesium-based Chip-Scale Atomic Clock (CSAC) and a rubidium-based Miniature Atomic Clock (MAC), both produced by Microchip.An example case of two spacecraft in a circular, low-Earth orbit receiving GPS position, GPS timing, and laser pulse time-of-flight measurements is simulated.The positions and velocities of the two spacecraft at a reference epoch and the constant clock model coefficients are estimated.Polynomial models of different orders were used as clock models. The effect of clock model order on the root-mean-square (RMS) of the clock error is apparent in the case using GPS and lasing measurements, showing that the clock model improves with increasing clock model order. Results compare the estimated clock model of a mission operation that only uses GPS measurements and a mission operation that uses both GPS and laser pulse time-of-flight measurements between spacecraft referenced to their on-board CSACs or on-board MACs. Including lasing measurements reduces the RMS clock model error by approximately 80% of the RMS of the cases with only GPS measurements.This simulation tool can be used to optimize the lasing operations schedule based on mission timing performance objectives. During this presentation, I will discuss the details of the simulation and clock model estimation, as well as discuss the results.I will compare the results of incorporating lasing timing measurements to using only GPS measurements and the outcomes of using different orders of polynomials.

Timing Performance↗

Single-tone parameter estimation from discrete-time observations

Estimation of the parameters of a single-frequency complex tone from a finite number of noisy discrete-time observations is discussed. The appropriate Cramer-Rao bounds and maximum-likelihood (ML) estimation algorithms are derived. Some properties of the ML estimators are proved. The relationship of ML estimation to the discrete Fourier transform is exploited to obtain practial algorithms. The threshold effect of one algorithm is analyzed and compared to simulation results. Other simulation results verify other aspects of the analysis.

Rife, D. C.↗

Real-Time Parameter Estimation Using Output Error

Output-error parameter estimation, normally a post- ight batch technique, was applied to real-time dynamic modeling problems. Variations on the traditional algorithm were investigated with the goal of making the method suitable for operation in real time. Im- plementation recommendations are given that are dependent on the modeling problem of interest. Application to ight test data showed that accurate parameter estimates and un- certainties for the short-period dynamics model were available every 2 s using time domain data, or every 3 s using frequency domain data. The data compatibility problem was also solved in real time, providing corrected sensor measurements every 4 s. If uncertainty corrections for colored residuals are omitted, this rate can be increased to every 0.5 s.

Grauer, Jared A.↗

Multiple Input Design for Real-Time Parameter Estimation in the Frequency Domain

A method for designing multiple inputs for real-time dynamic system identification in the frequency domain was developed and demonstrated. The designed inputs are mutually orthogonal in both the time and frequency domains, with reduced peak factors to provide good information content for relatively small amplitude excursions. The inputs are designed for selected frequency ranges, and therefore do not require a priori models. The experiment design approach was applied to identify linear dynamic models for the F-15 ACTIVE aircraft, which has multiple control effectors.

Morelli, Eugene↗

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗

Neural Network Would Estimate Fatigue Life

Processor learns mappings between sequences of loads and fatigue damage. Data processor built around neural network provides real-time estimates of fatigue life of simple mechanical components. Intended to be forerunner of more-advanced processors that estimates remaining lives of components stressed near their mechanical and thermal limits in turbine engines and other complicated, high-performance machinery. Real-time estimates of accumulated fatigue damage and remaining life before mechanical failure useful in scheduling maintenance and controlling operation to extend overall life of machinery.

Merrill, W.↗

Real-Time State Estimation of Structural Modes for an Aeroelastic Wind Tunnel Model

A method is presented for estimating displacements, velocities, and accelerations of structural modes in generalized coordinates from measured sensor data in real time. Specifically, strain data from conventional strain gauges and fiber optic strain sensors (FOSS) were combined with the strain modes (obtained from a finite element model) in a least-squares estimator to produce structural mode displacement estimates and uncertainties. Likewise, accelerometer data were combined with displacement mode shapes in a second least-squares estimator to produce structural mode acceleration estimates. A Kalman filter was then used to refine the displacement estimates and obtain velocity estimates. The method was applied to the half-span wind tunnel test article used in the NASA-Boeing collaboration called the Integrated Adaptive Wing Technology Maturation (IAWTM) project. The technique was found to be useful for real-time control and system identification applications.

Jared A. Grauer↗

Method for Real-Time State Estimation of Structural Modes for an Aeroelastic Wind Tunnel Model

A method for estimating displacements, velocities, and accelerations of structural modes in generalized coordinates from measured sensor data in real time is developed and demonstrated. Data from conventional strain gauges and fiber optic strain sensors (FOSS) were combined with strain mode shapes to produce least-squares estimates of the structural mode displacements. Similarly, accelerometer data were combined with displacement mode shapes to estimate structural mode accelerations. Estimates were then combined using a Kalman filter to refine the displacement estimates and produce structural mode velocity estimates. The approach was demonstrated using simulation data for the NASA-Boeing collaboration called the Integrated Adaptive Wing Technology Maturation (IAWTM) project in both a stable open-loop condition and an unstable condition where estimated displacement and velocity states were used for feedback control. Results supported the feasibility of using this approach for feedback control and system identification applications for wind tunnel tests.

Aeroservoelasticity↗

Precision Departure Release Capability (PDRC) Integration, Evaluation and Transition

NASA's Precision Departure Release Capability (PDRC) research activity is assessing the use of surface trajectory-based takeoff (OFF) time estimates for en route domain departure scheduling in Call For Release situations. Current aircraft departure release times computed by an Air Traffic Management decision support tool to meet Call For Release constraints are based on coarse estimates of aircraft OFF times. Uncertainty in OFF time estimates may result in missed opportunities to merge into an overhead stream and lost throughput. However, technology exists to improve OFF time estimates by using more accurate aircraft trajectory predictions available in NextGen airport surface management decision support tools. PDRC findings are expected to be broadly applicable to NextGen integrated arrival/departure/surface implementations. For concept development and evaluation purposes this research activity integrates NASA's Surface Management System (SMS) OFF time prediction capabilities with Traffic Management Advisor (TMA) departure scheduling functions. PDRC field evaluations are being conducted at the North Texas (NTX) Research Station. The NASA/FAA Integrated Arrival/Departure/Surface Research Transition Team is using PDRC as a pathfinder for developing technology transfer mechanisms and procedures.

Engelland, Shawn A.↗

Evaluation of 16 measures of mental workload using a simulated flight task emphasizing mediational activity

As aircraft and other systems become more automated, a shift is occurring in human operator participation in these systems. This shift is away from manual control and toward activities that tap the higher mental functioning of human operators. Therefore, an experiment was performed in a moving-base flight simulator to assess mediational (cognitive) workload measurement. Specifically, 16 workload estimation techniques were evaluated as to their sensitivity and intrusion in a flight task emphasizing mediational behavior. Task loading, using navigation problems presented on a display, was treated as an independent variable, and workload-measure values were treated as dependent variables. Results indicate that two mediational task measures, two rating scale measures, time estimation, and two eye behavior measures were reliably sensitive to mediational loading. The time estimation measure did, however, intrude on mediational task performance. Several of the remaining measures were completely insensitive to mediational load.

Wierwille, W. W.↗

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↗

A Geomagnetic Estimate of Mean Paleointensity

To test a statistical hypothesis about Earth's magnetic field against paleomagnetism, the present field is used to estimate time averaged paleointensity. The estimate uses the modem magnetic multipole spectrum R(n), which gives the mean square induction represented by spherical harmonics of degree n averaged over the sphere of radius a = 6371.2 km. The hypothesis asserts that the low degree multipole powers of the core-source field are distributed as chi-squared with 2n+l degrees of freedom and expectation values {R(n)} = K[(n+l/2)/n(n+l](c/a)(sup 2n+4), where c is the 3480 km radius of Earth's core. (This is compatible with a usually mainly geocentric axial dipolar field). Amplitude K is estimated by fitting theoretical to observational spectra through degree 12. The resulting calibrated expectation spectrum is summed through degree 12 to estimate expected square intensity {F(sup 2)}. The sum also estimates {F(sup 2)} averaged over geologic time, in so far as the present magnetic spectrum is a fair sample of that generated in the past by core geodynamic processes.

Voorhies, Coerte↗

A Geomagnetic Estimate of Mean Paleointensity

To test a statistical hypothesis about Earth's magnetic field against paleomagnetism, the present field is used to estimate time averaged paleointensity. The estimate used the modern magnetic multipole spectrum R(n), which gives the mean square induction represented by spherical harmonics of degree n averaged over the sphere of radius a = 6371.2 km. The hypothesis asserts that low degree multi-pole powers of the coresource field are distributed as chi-squared with 2n+1 degrees of freedom and expectation values, where c is the 3480 km radius of the Earth's core. (This is compatible with a usually mainly geocentric axial dipolar field). Amplitude K is estimated by fitting theoretical to observational spectra through degree 12. The resulting calibrated expectation spectrum is summed through degree 12 to estimate expected square intensity F(exp 2). The sum also estimates F(exp 2) averaged over geologic time, in so far as the present magnetic spectrum is a fair sample of that generated in the past by core geodynamic processes. Additional information is included in the original extended abstract.

Voorhies, Coerte V.↗

Real-Time Parameter Estimation for Flexible Aircraft

A method for estimating aeroelastic stability and control derivatives for flexible aircraft is developed and demonstrated using flight test data for the X-56A subscale demonstrator. The method uses the equation-error approach with frequency-domain data, and can be applied post-flight or in real time during flight. The non-dimensional aeroelastic forces and moments and the explanatory variables (including generalized displacement, rate, and acceleration states for the vibration modes) are estimated using a finite element model and onboard sensor measurements in both a least squares and Kalman filtering framework. The data are then transformed into the frequency domain for parameter estimation using equation error. This method can result in a more efficient analysis than with other iterative methods, and can leverage existing statistical tools for model structure determination, data collinearity detection, combining multiple maneuvers or prior information, and others to improve model quality.

Grauer, Jared A.↗

Fast-Time Simulation for Evaluating the Impact of Estimated Flight Ready Time Uncertainty on Surface Metering

NASA has been developing and demonstrating new concepts and technologies for Integrated Arrival, Departure, and Surface (IADS) traffic management capabilities under the Airspace Technology Demonstration 2 (ATD-2) project. One of the IADS capabilities in the ATD-2 project is surface metering enabled by a tactical surface scheduler to provide controllers with the pushback advisories for departures at gates. The tactical surface scheduler uses the estimated flight ready times provided by airlines, called Earliest Off-Block Times (EOBTs), as input to calculate the target off-block times for pushback advisories. However, the EOBTs are often inaccurate and deviate from the actual flight ready times, which may reduce the benefits of surface metering. In this paper, a linear regression model is developed to model the EOBT uncertainty distribution over time based on actual EOBT data collected at Charlotte airport. This EOBT model is integrated with a tactical surface scheduler and a fast-time simulation tool. To evaluate the impact of the EOBT accuracy on airport surface operations, fast-time simulations are implemented for selected traffic scenarios under different levels of modelled EOBT accuracy. The simulation results show that the EOBT uncertainty affects several performance metrics related to the surface metering, such as gate hold, taxi time reduction, and target takeoff time predictability, which in turn influences ATD-2's scheduler performance.

tactical surface scheduler↗

Fast-Time Simulation for Evaluating the Impact of Estimated Flight Ready Time Uncertainty on Surface Metering

NASA has been developing and demonstrating new concepts and technologies for Integrated Arrival, Departure, and Surface (IADS) traffic management capabilities under the Airspace Technology Demonstration 2 (ATD-2) project. One of the IADS capabilities in the ATD-2 project is surface metering enabled by a tactical surface scheduler to provide controllers with the pushback advisories for departures at gates. The tactical surface scheduler uses the estimated flight ready times provided by airlines, called Earliest Off-Block Times (EOBTs), as input to calculate the target off-block times for pushback advisories. However, the EOBTs are often inaccurate and deviate from the actual flight ready times, which may reduce the benefits of surface metering. In this paper, a linear regression model is developed to model the EOBT uncertainty distribution over time based on actual EOBT data collected at Charlotte airport. This EOBT model is integrated with a tactical surface scheduler and a fast-time simulation tool. To evaluate the impact of the EOBT accuracy on airport surface operations, fast-time simulations are implemented for selected traffic scenarios under different levels of modelled EOBT accuracy. The simulation results show that the EOBT uncertainty affects several performance metrics related to the surface metering, such as gate hold, taxi time reduction, and target takeoff time predictability, which in turn influences ATD-2's scheduler performance.

tactical surface scheduler↗

Precise optical pulse timing

This paper evaluates the performance of the maximum likelihood and several sub-optimum arrival time estimators for optical pulses. The performance analysis of a 'sliding window' counter followed by that of a threshold detector is achieved by deriving an expression for the first crossing density of the output of the counter. A sub-optimum arrival time estimator using a threshold rather than a maximum detector is suggested and evaluated by simulation. It appears to provide near optimum performance.

Lee, G.↗