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Results for “Earth rotation Kalman filter”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Extratropical aspects of the 40-50 day oscillation in length-of-day and atmospheric angular momentum

The 40-50-d oscillation in length-of-day (LOD) and AAM is investigated using the 12-yr overlap between two records: AAM data, compiled from the NMC, and LOD variation from the JPL Kalman-filtered earth rotation series. The NMC records are analyzed by latitude belts, in the light of the UCLA GCM results, in order to identify possibly distinct sources of the AAM oscillation in the midlatitudes and the tropics. Results suggest that two intraseasonal oscillations exist in the earth-atmosphere system: a tropical 50-d oscillation associated with the convectively driven waves of the type first described by Madden and Julian (1971) and a midlatitude 40-d oscillation associated with the interaction of nonzonal flow with topography.

Dickey, J. O.↗
NASA NTRS1986

Intercomparison of UT1 measurements during the MERIT campaign period

Universal time (UT1) measurements obtained using VLBI, lunar laser ranging (LLR), and BIH optical astronomy are compared. The JPL Kalman filter for the earth rotation and the polar motion is utilized to smooth one data set or combine data sets for intercomparison. The differences between raw UT1 data and independently smoothed data are employed to assess the accuracy of the measured series and the sufficiency of the error budget. Systematic errors in periodic signatures and length of day estimates are analyzed. The data reveal that the techniques of LLR and VLBI agree to within their formal errors. It is determined that the residual error for the LLR is too large and the residual error estimated for the BIH data is too small.

Spieth, M. A.↗
NASA NTRS2000

Combining Earth Orientation Measurements Using a Kalman Filter

A Kalman filter has many properties that make it an attractive choice as a technique for combining Earth orientation measurements. It allows the full accuracy of the measurements to be used, whether the measurements are degenerate or are of full rank, are irregularly or regularly spaced in time, or are corrupted by systematic or other errors that can be described by stochastic models.

Earth rotation combination Kalman filter↗
NASA NTRS1980

The influence of orbit selection on the accuracy of the Stanford Relativity gyroscope experiment

This paper discusses an error analysis for the Stanford Relativity experiment, designed to measure the precession of a gyroscope's spin-axis predicted by general relativity. Measurements will be made of the spin-axis orientations of 4 superconducting spherical gyroscopes carried by an earth-satellite. Two relativistic precessions are predicted: a 'geodetic' precession associated with the satellite's orbital motion and a 'motional' precession due to the earth's rotation. Using a Kalman filter covariance analysis with a realistic error model we have computed the error in determining the relativistic precession rates. Studies show that a slightly off-polar orbit is better than a polar orbit for determining the 'motional' drift.

Vassar, R.↗
NASA NTRS1998

Combinations of EOP Measurements: COMB97 & POLE97

A Kalman filter has been used to combine independent measurements of the Earth's orientation taken by optical astrometry and the space-geodetic techniques of LLR, SLR, VLBI, and GPS.

EOP measurements Kalman filter Earth orientation E↗
NASA NTRS1992

Sub-daily resolution of earth rotation variations with Global Positioning System measurements

Data from a worldwide Global Positioning System (GPS) tracking experiment have been used to determine variations in earth rotation (UT1-UTC) over a time period of three weeks. Kalman filtering and smoothing enabled changes in UT1-UTC over intervals of 2 to 24 hrs to be detected with the GPS data. Internal consistency checks and comparisons with other solutions from very long baseline interferometry (VLBI) and satellite laser ranging (SLR) indicate that the GPS UT1-UTC estimates are accurate to about 2 cm. Comparison of GPS-estimated variations in UT1-UTC with 2-hr time resolution over 4 days with predicted variations computed from diurnal and semidiurnal oceanic tidal contributions strongly suggests that the observed periodic sub-daily variations of about 0.1 msec are largely of tidal origin.

Lichten, Stephen M.↗
NASA NTRS1994

The short-term prediction of universal time and length of day using atmospheric angular momentum

The ability to predict short-term variations in the Earth's rotation has gained importance in recent years owing to more precise spacecraft tracking requirements. Universal time (UT1), that component of the Earth's orientation corresponding to the rotation angle, can be measured by number of high-precision space geodetic techniques. A Kalman filter developed at the Jet Propulsion Laboratory (JPL) optimally combines these different data sets and generates a smoothed times series and a set of predictions for UT1, as well as for additional Earth orientation components. These UT1 predictions utilize an empirically derived random walk stochastic model for the length of the day (LOD) and require frequent and up-to-date measurements of either UT1 or LOD to keep errors from quickly accumulating. Recent studies have shown that LOD variations are correlated with changes in the Earth's axial atmospheric angular momentum (AAM) over timescales of several years down to as little as 8 days. AAM estimates and forecasts out to 10 days are routinely available from meteorological analysis centers; these data can supplement geodetic measurements to improve the short-term prediction of LOD and have therefore been incorporated as independent data types in the JPL Kalman filter. We find that AAM and, to a lesser extent, AAM forecast data are extremely helpful in generating accurate near-real-time estimates of UT1 and LOD and in improving short-term predictions of these quantities out to about 10 days.

Freedman, A. P.↗
NASA NTRS1993

Determination of the Excess Length-of-Day Since 1630

The Kalman Earth Orientation Filter (KEOF) is used at JPL to combine independent observations of the Earth's rotation parameters, producing smoothed, interpolated estimates of polar motion (PM) and UT1-TAI, as well as estimates of their excitation functions such as the length-of-day (lod). Prior to their combination, adjustments to the data sets are made in order to place them within a common reference frame. Recently, a number of lod data sets have been determined at JPL by combining the observations summarized in Table 1. The resulting lod data sets, summarized in Table 2, span different time intervals depending upon the particular subset of observations being combined, with the longest series, spanning 1630-1990, being obtained by combining all of the observations. The determination of these lod data sets and their comparison with series of climate indicators such as the Southern Oscillation Index will be discussed.

Gross, R. S.↗
NASA NTRS1983

Filtering flight data prior to aerodynamic system identification

An algorithm for processing flight-test data to provide state estimates and instrument calibrations for aerodynamic or hydrodynamic system identification by the equation error estimation method is developed and demonstrated on synthesized data. The extended-Kalman-filter algorithm employs a locally level, north-pointing frame of reference, accounts for rotating ellipsoidal earth effects, and estimates sensor bias, scale factors, wind components, and process noise levels by maximum-likelihood parameters. The method is found to be most effective with navigation quality inertial input data. The algorithm is applied to data from a six-degree-of-freedom F-4 aircraft simulation and shown to produce state estimates in good agreement with the simulation values.

Trankle, T. L.↗
NASA NTRS2023

Extended Kalman Filter Performance on the Artemis-1 Mission

The Artemis Program is NASA’s campaign to explore the Moon and beyond. Artemis-1, the uncrewed exoLEO test flight of the Orion spacecraft, was completed in 2022. There were four navigation Extended Kalman Filters (EKFs) that are part of the Orion navigation system. The Atmospheric Extended Kalman Filter (ATMEKF) estimates the vehicle position, velocity, and attitude (referred to as the vehicle state) during the ascent and entry phases of flight. Once Orion is outside of Earths atmosphere, the Earth Orbit Extended Kalman Filter (EOEKF) and CisLunar Extended Kalman Filter (CLEKF) estimate the translational states, depending on the phase of flight, while the Attitude Extended Kalman Filter (ATTEKF) estimates the rotational state of the vehicle. The Kalman filters propagate the vehicle state forward in time using a combination of dynamics models and the output data from the Inertial Measurement Unit (IMU). The filters update the vehicle states and associated uncertainties, in the form of the covariance matrix, using pseudorange measurements from GPS (in ATMEKF/EOEKF), optical navigation measurements of the Earth or Moon (in CLEKF), and star tracker measurements (in ATTEKF). Simultaneously, the Kalman filters estimate error sources in the sensors, which are included in the state vectors as Exponentially Correlated Random Variables (ECRVs). This paper will summarize the performance of these filters during the Artemis-1 mission.

Artemis-1↗
NASA NTRS1989

Extended Kalman filter for attitude estimation of the earth radiation budget satellite

The design and testing of an Extended Kalman Filter (EKF) for ground attitude determination, misalignment estimation and sensor calibration of the Earth Radiation Budget Satellite (ERBS) are described. Attitude is represented by the quaternion of rotation and the attitude estimation error is defined as an additive error. Quaternion normalization is used for increasing the convergence rate and for minimizing the need for filter tuning. The development of the filter dynamic model, the gyro error model and the measurement models of the Sun sensors, the IR horizon scanner and the magnetometers which are used to generate vector measurements are also presented. The filter is applied to real data transmitted by ERBS sensors. Results are presented and analyzed and the EKF advantages as well as sensitivities are discussed. On the whole the filter meets the expected synergism, accuracy and robustness.

Deutschmann, Julie↗
NASA NTRS1990

Extended Kalman Filter for attitude estimation of the Earth Radiation Budget Satellite

The design and testing of an Extended Kalman Filter (EKF) for ground attitude determination, misalignment estimation and sensor calibration of the Earth Radiation Budget Satellite (ERBS) are described. Attitude is represented by the quaternion of rotation and the attitude estimation error is defined as an additive error. Quaternion normalization is used for increasing the convergence rate and for minimizing the need for filter tuning. The development of the filter dynamic model, the gyro error model and the measurement models of the sun sensors, the IR horizon scanner and the magnetometers which are used to generate vector measurements are also presented. The filter is applied to real data transmitted by ERBS sensors. Results are presented and analyzed and the EKF advantages as well as sensitivities are discussed. On the whole the filter meets the expected synergism, accuracy and robustness.

Deutschmann, Julie↗
NASA NTRS1999

A Combined Length-of-day Series Spanning 1832-1997: Lunar97

Universal time measurements taken by the lunar occultation, optical astrometry, lunar laser ranging, and very long baseline interferometry have been combined using a Kalman filter to produce a smoothed length-of-day series, LUNAR97, spanning 1832.5-1997.5 at yearly intervals.

Earth↗
NASA NTRS1992

Vision-based recursive estimation of rotorcraft obstacle locations

The authors address vision-based passive ranging during nap-of-the-earth (NOE) rotorcraft flight. They consider the problem of estimating the relative location of identifiable features on nearby obstacles, assuming a sequence of noisy camera images and imperfect measurements of the camera's translation and rotation. An iterated extended Kalman filter is used to provide recursive range estimation. The correspondence problem is simplified by predicting and tracking each feature's image within the Kalman filter framework. Simulation results are presented which show convergent estimates and generally successful feature point tracking. Estimation performance degrades for features near the optical axis and for accelerating motions. Image tracking is also sensitive to angular rate.

Leblanc, D. J.↗