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Results for “state 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.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Extended Kalman Filter for MMS State Estimation

The Magnetospheric MultiScale Mission is a four spacecraft formation flying mission designed to study the Earth s magnetosphere. The spacecraft fly in highly elliptical orbits, forming a tetrahedron at apogee. Each spacecraft spins at 3 RPM and is equipped with a star scanner, slit sun sensor, and accelerometer. The purpose of this work is to develop an Extended Kalman Filter to simultaneously estimate the attitude, angular velocity, angular acceleration, and center of mass of each spacecraft.

Markley, F. Landis↗
NASA NTRSDate not supplied

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↗
NASA NTRS1997

On-board State Estimation for Planetary Aerobots

Oscillatory balloon systems with telerobotic capabilities are being studied to support future space exploration by probes which will move up and down in a planetary atmosphere, land and explore numerous surface sites.

Planetary↗
NASA NTRSDate not supplied

Hybrid Flush and Synthetic Air Data Filter for Entry Vehicle Atmospheric State Estimation

A hybrid flush/synthetic air data sensing filter utilizing Kalman-Schmidt and Rach-Tung-Striebel smoothers is developed to obtain entry vehicle atmosphere estimates. The filter/smoother blends information from pressure sensors distributed on the heatshield with measurements of the vehicle aerodynamic forces and moments computed from mass properties and inertial measurement unit data, and prior estimates of the atmosphere. The filter produces estimates of the atmospheric conditions along the entry trajectory, and systematic error estimates to reconcile differences between the pressure and aerodynamic data sources. The filter is applied to data acquired during the Mars Science Laboratory and Mars 2020 entry, descent, and landing at Gale crater and at Jezero crater, respectively. The results show that the hybrid filter produces estimates of the freestream flight condition with lower uncertainty than either the flush or synthetic air data algorithms. The filter accomplishes this result by incorporating additional data and computing estimates of systematic error parameters in the pressure data and the aerodynamic model to further reduce the uncertainties.

Christopher D. Karlgaard↗
NASA NTRSDate not supplied

Hybrid Flush and Synthetic Air Data Filter for Entry Vehicle Atmospheric State Estimation

A hybrid flush/synthetic air data sensing filter for entry vehicle atmosphere estimation is developed. The approach makes use of a Kalman-Schmidt and Rauch-Tung-Streibel smoother. The filter/smoother blends information from pressure sensors distributed on the heatshield with pseudo-measurements of the vehicle aerodynamic forces and moments computed from mass properties and inertial measurement unit data, and prior estimates of the atmosphere. The filter produces estimates of the atmospheric conditions along the entry trajectory, and systematic error estimates to reconcile differences between the pressure and aerodynamic data sources. The filter is applied to data acquired during the Mars 2020 entry, descent, and landing at Jezero crater on February 18th, 2021. The final paper will also include results from the Mars Science Laboratory entry, descent, and landing.

Chris D. Karlgaard↗
NASA NTRSDate not supplied

Empirical State Error Covariance Matrix for Batch Estimation

State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their abilities to describe the true uncertainty in the estimated states. By a reinterpretation of the equations involved in the weighted least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. This proposed empirical state error covariance matrix will contain the effect of all error sources, known or unknown. Results are presented for a simple, two observer, measurement error only problem.

Estimation↗
NASA NTRS2015

Empirical State Error Covariance Matrix for Batch Estimation

State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the uncertainty in the estimated states. By a reinterpretation of the equations involved in the weighted batch least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. The proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. This empirical error covariance matrix may be calculated as a side computation for each unique batch solution. Results based on the proposed technique will be presented for a simple, two observer and measurement error only problem.

Frisbee, Joe↗
NASA NTRSDate not supplied

Maneuver Reconstruction, Tracking Scheduling, and Bias Estimation in the Near Rectilinear Halo Orbit (NRHO)

NASA’s Gateway program will build a crew-tended station in an Earth-Moon Near Rectilinear Halo Orbit (NRHO) to support deep space missions to the lunar surface and heliocentric space. The station in the NRHO will be tracked by the Deep Space Network (DSN) with 2-way radiometric tracking data to produce an estimated state that is utilized to target Orbit Maintenance Maneuvers (OMMs). However, the tracking data is corrupted with measurement noise and range bias, which results in state estimation error and OMM execution error. This paper reports on analysis performed to improve the geometry of the tracking data problem with the inclusion of cross-hemispheric partner sites and explores the performance impact of handovers between partner sites and DSN sites. The OMM execution error is estimated directly using a Batch Least Squares (BLS) process, and the range bias estimation is analyzed among both original DSN sites and a combined DSN + partner sites scenario.

Clark P Newman↗