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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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NASA NTRS1972

Optimal and suboptimal methods of satellite surveillance for traffic control of transoceanic flights.

Description of a system which uses two synchronous equatorial satellites. Aircraft location is determined by measuring the range of the aircraft to each satellite as well as aircraft altitude, and transmitting this information periodically to a ground station. There, the aircraft's geographic position at the time of transmission is computed. This information is then combined with past position measurements in a suboptimal filter to determine aircraft position and velocity (the velocity being used to estimate aircraft position between transmissions). The suboptimal filter is a simplification of the optimal Kalman filter. Except for altitude information, the system is independent of the aircraft navigation system.

Winter, H.↗

NASA NTRS1979

Navigation systems for approach and landing of VTOL aircraft

The formulation and implementation of navigation systems used for research investigations in the V/STOLAND avionics system are described. The navigation systems prove position and velocity in a cartestian reference frame aligned with the runway. They use filtering techniques to combine the raw position data from navaids (e.g., TACAN, MLS) with data from onboard inertial sensors. The filtering techniques which use both complementary and Kalman filters, are described. The software for the navigation systems is also described.

Schmidt, S. F.↗

NASA NTRS2002

The Estimation Theory Framework of Data Assimilation

Lecture 1. The Estimation Theory Framework of Data Assimilation: 1. The basic framework: dynamical and observation models; 2. Assumptions and approximations; 3. The filtering, smoothing, and prediction problems; 4. Discrete Kalman filter and smoother algorithms; and 5. Example: A retrospective data assimilation system

Cohn, S.↗

NASA NTRSDate not supplied

An Efficient Filter for Measurements Corrupted with Cauchy Noise

This paper present a new sequential filter for state estimation using measurements corrupted with Cauchy noise. The new filter retains the familiar structure of the Kalman filter and is computationally efficient. In addition, it does not exhibit computational complexity which grows or varies in time like existing methods. These results are based upon a nearly 50 year old result by Masreliez in which the conditional mean estimator is approximated via linearization of the measurement predictive density. This work derives the new filter, provides discussion regarding practical implementation, and present Monte Carlo analyses to validate and assess the new filter's performance.

James S McCabe↗

NASA NTRS2004

A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.

Turso, James A.↗

NASA NTRS1995

Combinations of Earth Orientation Measurements: COMB94 and POLE94

A Kalman filter has been used to combine Earth orientation measurements taken by optical astrometry with SPACE94, a previously determined combination of Earth orientation measurements taken by space-geodetic techniques. This created two series of combined, smoothed, and interpolated polar motion: COMB94 covering 1962-1995 at 5-day intervals, and POLE94 covering 1900 to 1995 at 30.4375-day intervals.

Earth↗

NASA NTRS2020

SNPP and NOAA-20 VIIRS On-Orbit Geolocation Trending and Improvements

Two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors have been in operations for more than 8.5 and 2.5 years since they were launched in October 2011 on SNPP satellite and in November 2017 on NOAA-20 satellite, respectively. These are two satellites in the Join Polar Satellite System (JPSS) constellation, of which Suomi National Polar-orbiting Partnership (SNPP) is a risk reduction satellite and NOAA-20 is the first of four JPSS satellites(JPSS-1 became NOAA-20 after launch). Accurate geolocation is a critical element in data calibration for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved geolocation accuracy in VIIRS Collection-1 (C1) and C2 re-processing. We implemented a VIIRS instrument geometric model update (VIGMU)for both sensors that correct for geolocation error oscilations in the scan direction. We borrowed code from Moderate Resolution Imaging Spectroradiometer (MODIS) geolocation software to correct for time-dependent pointing variations, that are particularly acute in NOAA-20 VIIRS, and some pointing anomalies in SNPP VIIRS. We developed a Kalman Filter using gyrodata to correct for attitude errors due to the degradation of the star trackers performance from the SNPP satellite. We also present an improved ground control point matching (CPM) tool, in which the ground control point (GCP) chips library is refreshed using recently launched Landsat-8 images.

SNPP↗

NASA NTRS1995

A Combination of Earth Orientation Measurements: SPACE94

space-geodetic↗

NASA NTRS2021

Mars Exploration Rovers EDL Trajectory and Atmosphere Reconstruction using NewSTEP

This document describes the trajectory and atmosphere reconstruction of the Mars Exploration Rovers (Spirit and Opportunity) Entry, Descent, and Landing using the New Statistical Trajectory Estimation Program. The approach utilizes a Kalman filter to blend inertial measurement unit data with initial conditions and radar altimetry to obtain the inertial trajectory of the entry vehicle. The nominal aerodynamic database is then used in combination with the sensed accelerations to obtain estimates of the atmosphere-relative state. The reconstructed atmosphere profile is then blended with pre-flight models to construct an estimate of the as-flown atmosphere.

EDL↗

NASA NTRS1996

Combinations of Earth Orientation Measurements: SPACE94, COMB94, and POLE94

A Kalman filter has been used to combine independent measurements of the Earth's orientation taken by the space-geodetic observing techniques of lunar laser ranging, satellite laser ranging, very long baseline interferometry, and the Global Positioning System. Prior to their combination, the data series were adjusted to have the same bias and rate, the stated uncertainties of the measurements were adjusted, and data points considered to be outliers were deleted. The resulting combination, SPACE94, consists of smoothed, interpolated polar motion and UT1-UTC values spanning October 6.0, 1976, to January 27.0, 1995, at 1-day intervals.

Kalman↗

NASA NTRS2009

Visual Target Tracking in the Presence of Unknown Observer Motion

Much attention has been given to the visual tracking problem due to its obvious uses in military surveillance. However, visual tracking is complicated by the presence of motion of the observer in addition to the target motion, especially when the image changes caused by the observer motion are large compared to those caused by the target motion. Techniques for estimating the motion of the observer based on image registration techniques and Kalman filtering are presented and simulated. With the effects of the observer motion removed, an additional phase is implemented to track individual targets. This tracking method is demonstrated on an image stream from a buoy-mounted or periscope-mounted camera, where large inter-frame displacements are present due to the wave action on the camera. This system has been shown to be effective at tracking and predicting the global position of a planar vehicle (boat) being observed from a single, out-of-plane camera. Finally, the tracking system has been extended to a multi-target scenario.

image mosaic↗

NASA NTRS2021

Mathematical Characterization of Battery Models

The purpose of this document is to demonstrate the use of the Extended Kalman Filter as a tool for battery state estimation and the estimation of battery state of charge. The mathematical details based on the equivalent circuit model are presented followed by an electrochemical engineering model. A simplified first-order model is used to demonstrate the procedure followed by second and third-order models. Next a simplified electrochemistry model is presented along with observer development. State observability is calculated for the simpler equivalent circuit models and the simplified electrochemistry model. An outline of the battery model parameter identification method is presented, and model performance based on experimental and flight data is demonstrated.

Battery↗

NASA NTRS2011

Model-Based Wavefront Control for CCAT

The 25-m aperture CCAT submillimeter-wave telescope will have a primary mirror that is divided into 162 individual segments, each of which is provided with 3 positioning actuators. CCAT will be equipped with innovative Imaging Displacement Sensors (IDS) inexpensive optical edge sensors capable of accurately measuring all segment relative motions. These measurements are used in a Kalman-filter-based Optical State Estimator to estimate wavefront errors, permitting use of a minimum-wavefront controller without direct wavefront measurement. This controller corrects the optical impact of errors in 6 degrees of freedom per segment, including lateral translations of the segments, using only the 3 actuated degrees of freedom per segment. The global motions of the Primary and Secondary Mirrors are not measured by the edge sensors. These are controlled using a gravity-sag look-up table. Predicted performance is illustrated by simulated response to errors such as gravity sag.

edge sensor↗

NASA NTRS1976

Triangular covariance factorizations for

An improved computational form of the discrete Kalman filter is derived using an upper triangular factorization of the error covariance matrix. The covariance P is factored such that P = UDUT where U is unit upper triangular and D is diagonal. Recursions are developed for propagating the U-D covariance factors together with the corresponding state estimate. The resulting algorithm, referred to as the U-D filter, combines the superior numerical precision of square root filtering techniques with an efficiency comparable to that of Kalman's original formula. Moreover, this method is easily implemented and involves no more computer storage than the Kalman algorithm. These characteristics make the U-D method an attractive realtime filtering technique. A new covariance error analysis technique is obtained from an extension of the U-D filter equations. This evaluation method is flexible and efficient and may provide significantly improved numerical results. Cost comparisons show that for a large class of problems the U-D evaluation algorithm is noticeably less expensive than conventional error analysis methods.

Thornton, C. L.↗

NASA NTRS2013

Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation

Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time.

Data Assimilation↗

NASA NTRS1978

Filtering and error analysis via the UDU super T covariance factorization

Kalman filter algorithms based on the UDU super T covariance factorization are discussed, with special attention given to algorithm implementation efficiency. A U-D-factored covariance error-analysis algorithm is formulated, and its efficiency and numerical stability are demonstrated in a representative orbit determination problem. The numerical results are compared with those obtained using covariance error-analysis formulas, and the comparison highlights the numerical superiority of the present algorithm. A byproduct of the U-D analysis is a highly efficient algorithm mechanization of the arbitrary gain covariance update formula.

Thornton, C. L.↗

NASA NTRS2003

SIRTF Focal Plane Survey: A Pre-flight Error Analysis

This report contains a pre-flight error analysis of the calibration accuracies expected from implementing the currently planned SIRTF focal plane survey strategy. The main purpose of this study is to verify that the planned strategy will meet focal plane survey calibration requirements (as put forth in the SIRTF IOC-SV Mission Plan [4]), and to quantify the actual accuracies expected. The error analysis was performed by running the Instrument Pointing Frame (IPF) Kalman filter on a complete set of simulated IOC-SV survey data, and studying the resulting propagated covariances. The main conclusion of this study is that the all focal plane calibration requirements can be met with the currently planned survey strategy. The associated margins range from 3 to 95 percent, and tend to be smallest for frames having a 0.14" requirement, and largest for frames having a more generous 0.28" (or larger) requirement. The smallest margin of 3 percent is associated with the IRAC 3.6 and 5.8 micron array centers (frames 068 and 069), and the largest margin of 95 percent is associated with the MIPS 160 micron array center (frame 087). For pointing purposes, the most critical calibrations are for the IRS Peakup sweet spots and short wavelength slit centers (frames 019, 023, 052, 028, 034). Results show that these frames are meeting their 0.14" requirements with an expected accuracy of approximately 0.1", which corresponds to a 28 percent margin.

pointing↗

NASA NTRS2022

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km^2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology.

soil moisture↗