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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Attitude Error Representations for Kalman Filtering

The quaternion has the lowest dimensionality possible for a globally nonsingular attitude representation. The quaternion must obey a unit norm constraint, though, which has led to the development of an extended Kalman filter using a quaternion for the global attitude estimate and a three-component representation for attitude errors. We consider various attitude error representations for this Multiplicative Extended Kalman Filter and its second-order extension.

Markley, F. Landis↗
NASA NTRS2002

Recent Flight Results of the TRMM Kalman Filter

The Tropical Rainfall Measuring Mission (TRMM) spacecraft is a nadir pointing spacecraft that nominally controls the roll and pitch attitude based on the Earth Sensor Assembly (ESA) output. TRMM's nominal orbit altitude was 350 km, until raised to 402 km to prolong mission life. During the boost, the ESA experienced a decreasing signal to noise ratio, until sun interference at 393 km altitude made the ESA data unreliable for attitude determination. At that point, the backup attitude determination algorithm, an extended Kalman filter, was enabled. After the boost finished, TRMM reacquired its nadir-pointing attitude, and continued its mission. This paper will briefly discuss the boost and the decision to turn on the backup attitude determination algorithm. A description of the extended Kalman filter algorithm will be given. In addition, flight results from analyzing attitude data and the results of software changes made onboard TRMM will be discussed. Some lessons learned are presented.

Andrews, Stephen F.↗
NASA NTRS2003

Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters

In this report, a fault detection and isolation (FDI) system which utilizes a bank of Kalman filters is developed for aircraft engine sensor and actuator FDI in conjunction with the detection of component faults. This FDI approach uses multiple Kalman filters, each of which is designed based on a specific hypothesis for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The performance of the FDI system is evaluated against a nonlinear engine simulation for various engine faults at cruise operating conditions. In order to mimic the real engine environment, the nonlinear simulation is executed not only at the nominal, or healthy, condition but also at aged conditions. When the FDI system designed at the healthy condition is applied to an aged engine, the effectiveness of the FDI system is impacted by the mismatch in the engine health condition. Depending on its severity, this mismatch can cause the FDI system to generate incorrect diagnostic results, such as false alarms and missed detections. To partially recover the nominal performance, two approaches, which incorporate information regarding the engine s aging condition in the FDI system, will be discussed and evaluated. The results indicate that the proposed FDI system is promising for reliable diagnostics of aircraft engines.

Kobayashi, Takahisa↗
NASA NTRS2003

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

Kobayashi, Takahisa↗
NASA NTRS2003

Control of Thermo-Acoustics Instabilities: The Multi-Scale Extended Kalman Approach

"Multi-Scale Extended Kalman" (MSEK) is a novel model-based control approach recently found to be effective for suppressing combustion instabilities in gas turbines. A control law formulated in this approach for fuel modulation demonstrated steady suppression of a high-frequency combustion instability (less than 500Hz) in a liquid-fuel combustion test rig under engine-realistic conditions. To make-up for severe transport-delays on control effect, the MSEK controller combines a wavelet -like Multi-Scale analysis and an Extended Kalman Observer to predict the thermo-acoustic states of combustion pressure perturbations. The commanded fuel modulation is composed of a damper action based on the predicted states, and a tones suppression action based on the Multi-Scale estimation of thermal excitations and other transient disturbances. The controller performs automatic adjustments of the gain and phase of these actions to minimize the Time-Scale Averaged Variances of the pressures inside the combustion zone and upstream of the injector. The successful demonstration of Active Combustion Control with this MSEK controller completed an important NASA milestone for the current research in advanced combustion technologies.

Le, Dzu K.↗
NASA NTRS2002

Applications of Kalman filtering to real-time trace gas concentration measurements

A Kalman filtering technique is applied to the simultaneous detection of NH3 and CO2 with a diode-laser-based sensor operating at 1.53 micrometers. This technique is developed for improving the sensitivity and precision of trace gas concentration levels based on direct overtone laser absorption spectroscopy in the presence of various sensor noise sources. Filter performance is demonstrated to be adaptive to real-time noise and data statistics. Additionally, filter operation is successfully performed with dynamic ranges differing by three orders of magnitude. Details of Kalman filter theory applied to the acquired spectroscopic data are discussed. The effectiveness of this technique is evaluated by performing NH3 and CO2 concentration measurements and utilizing it to monitor varying ammonia and carbon dioxide levels in a bioreactor for water reprocessing, located at the NASA-Johnson Space Center. Results indicate a sensitivity enhancement of six times, in terms of improved minimum detectable absorption by the gas sensor.

Non-NASA Center↗
NASA NTRS2005

An Extension to the Kalman Filter for an Improved Detection of Unknown Behavior

The use of Kalman filter (KF) interferes with fault detection algorithms based on the residual between estimated and measured variables, since the measured values are used to update the estimates. This feedback results in the estimates being pulled closer to the measured values, influencing the residuals in the process. Here we present a fault detection scheme for systems that are being tracked by a KF. Our approach combines an open-loop prediction over an adaptive window and an information-based measure of the deviation of the Kalman estimate from the prediction to improve fault detection.

Benazera, Emmanuel↗
NASA NTRS2005

Application of a Constant Gain Extended Kalman Filter for In-Flight Estimation of Aircraft Engine Performance Parameters

An approach based on the Constant Gain Extended Kalman Filter (CGEKF) technique is investigated for the in-flight estimation of non-measurable performance parameters of aircraft engines. Performance parameters, such as thrust and stall margins, provide crucial information for operating an aircraft engine in a safe and efficient manner, but they cannot be directly measured during flight. A technique to accurately estimate these parameters is, therefore, essential for further enhancement of engine operation. In this paper, a CGEKF is developed by combining an on-board engine model and a single Kalman gain matrix. In order to make the on-board engine model adaptive to the real engine s performance variations due to degradation or anomalies, the CGEKF is designed with the ability to adjust its performance through the adjustment of artificial parameters called tuning parameters. With this design approach, the CGEKF can maintain accurate estimation performance when it is applied to aircraft engines at offnominal conditions. The performance of the CGEKF is evaluated in a simulation environment using numerous component degradation and fault scenarios at multiple operating conditions.

Kobayashi, Takahisa↗
NASA NTRS2006

Flexible Generation of Kalman Filter Code

Domain-specific program synthesis can automatically generate high quality code in complex domains from succinct specifications, but the range of programs which can be generated by a given synthesis system is typically narrow. Obtaining code which falls outside this narrow scope necessitates either 1) extension of the code generator, which is usually very expensive, or 2) manual modification of the generated code, which is often difficult and which must be redone whenever changes are made to the program specification. In this paper, we describe adaptations and extensions of the AUTOFILTER Kalman filter synthesis system which greatly extend the range of programs which can be generated. Users augment the input specification with a specification of code fragments and how those fragments should interleave with or replace parts of the synthesized filter. This allows users to generate a much wider range of programs without their needing to modify the synthesis system or edit generated code. We demonstrate the usefulness of the approach by applying it to the synthesis of a complex state estimator which combines code from several Kalman filters with user-specified code. The work described in this paper allows the complex design decisions necessary for real-world applications to be reflected in the synthesized code. When executed on simulated input data, the generated state estimator was found to produce comparable estimates to those produced by a handcoded estimator

Richardson, Julian↗
NASA NTRS2006

Kalman Filtering of Angular-Momentum-Based Attitude Parameters

This paper presents an extended Kalman filter using an attitude parameterization that is advantageous for attitude estimation of spinning spacecraft. The parameters are the angular momentum components in an inertial reference frame, the angular momentum components in the body frame, and a rotation angle. To avoid the singularity of the 7x7 covariance of this state vector arising from the constraint that the magnitude of the angular momentum vector is the same in the inertial and body frames, the Kalman filter employs the nonsingular 6x6 covariance of a reduced error state. Three of the components of this six-component error state are the usual infinitesimal attitude error angles, so the usual 3x3 attitude covariance matrix is a submatrix of the 6x6 covariance. The performance of the resulting filter is compared with that of a quaternion-based filter.

Markley, F. Landis↗
NASA NTRS2007

Tracking and Data Relay Satellite (TDRS) Orbit Estimation Using an Extended Kalman Filter

Alternatives to the Tracking and Data Relay Satellite (TDRS) orbit estimation procedure were studied to develop a technique that both produces more reliable results and is more amenable to automation than the prior procedure. The Earth Observing System (EOS) Terra mission has TDRS ephemeris prediction 3(sigma) requirements of 75 meters in position and 5.5 millimeters per second in velocity over a 1.5-day prediction span. Meeting these requirements sometimes required reruns of the prior orbit determination (OD) process, with manual editing of tracking data to get an acceptable solution. After a study of the available alternatives, the Flight Dynamics Facility (FDF) began using the Real-Time Orbit Determination (RTOD(Registered TradeMark)) Kalman filter program for operational support of TDRSs in February 2007. This extended Kalman filter (EKF) is used for daily support, including within hours after most thrusting, to estimate the spacecraft position, velocity, and solar radiation coefficient of reflectivity (C(sub R)). The tracking data used are from the Bilateration Ranging Transponder System (BRTS), selected TDRS System (TDRSS) User satellite tracking data, and Telemetry, Tracking, and Command (TT&C) data. Degraded filter results right after maneuvers and some momentum unloads provided incentive for a hybrid OD technique. The results of combining EKF strengths with the Goddard Trajectory Determination System (GTDS) Differential Correction (DC) program batch-least-squares solutions, as recommended in a 2005 paper on the chain-bias technique, are also presented.

Ward, Douglas T.↗
NASA NTRS2010

Comparison of Sigma-Point and Extended Kalman Filters on a Realistic Orbit Determination Scenario

Sigma-point filters have received a lot of attention in recent years as a better alternative to extended Kalman filters for highly nonlinear problems. In this paper, we compare the performance of the additive divided difference sigma-point filter to the extended Kalman filter when applied to orbit determination of a realistic operational scenario based on the Interstellar Boundary Explorer mission. For the scenario studied, both filters provided equivalent results. The performance of each is discussed in detail.

Gaebler, John↗
NASA NTRS2006

Kalman Filter for Calibrating a Telescope Focal Plane

The instrument-pointing frame (IPF) Kalman filter, and an algorithm that implements this filter, have been devised for calibrating the focal plane of a telescope. As used here, calibration signifies, more specifically, a combination of measurements and calculations directed toward ensuring accuracy in aiming the telescope and determining the locations of objects imaged in various arrays of photodetectors in instruments located on the focal plane. The IPF Kalman filter was originally intended for application to a spaceborne infrared astronomical telescope, but can also be applied to other spaceborne and ground-based telescopes. In the traditional approach to calibration of a telescope, (1) one team of experts concentrates on estimating parameters (e.g., pointing alignments and gyroscope drifts) that are classified as being of primarily an engineering nature, (2) another team of experts concentrates on estimating calibration parameters (e.g., plate scales and optical distortions) that are classified as being primarily of a scientific nature, and (3) the two teams repeatedly exchange data in an iterative process in which each team refines its estimates with the help of the data provided by the other team. This iterative process is inefficient and uneconomical because it is time-consuming and entails the maintenance of two survey teams and the development of computer programs specific to the requirements of each team. Moreover, theoretical analysis reveals that the engineering/ science iterative approach is not optimal in that it does not yield the best estimates of focal-plane parameters and, depending on the application, may not even enable convergence toward a set of estimates.

Kang, Bryan↗
NASA NTRS2016

Spacecraft Dynamics Should be Considered in Kalman Filter Attitude Estimation

Kalman filter based spacecraft attitude estimation has been used in some high-profile missions and has been widely discussed in literature. While some models in spacecraft attitude estimation include spacecraft dynamics, most do not. To our best knowledge, there is no comparison on which model is a better choice. In this paper, we discuss the reasons why spacecraft dynamics should be considered in the Kalman filter based spacecraft attitude estimation problem. We also propose a reduced quaternion spacecraft dynamics model which admits additive noise. Geometry of the reduced quaternion model and the additive noise are discussed. This treatment is more elegant in mathematics and easier in computation. We use some simulation example to verify our claims.

Yang, Yaguang↗
NASA NTRS2020

Assimilation of SMAP and ASCAT Soil Moisture Retrievals into the JULES Land Surface Model Using the Local Ensemble Transform Kalman Filter

A land data assimilation system is developed to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble Transform Kalman Filter (LETKF). The system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting. The soil moisture assimilation estimates are evaluated with ground-based soil moisture measurements over the continental U.S. for five consecutive warm seasons (May–September of 2015–2019). The result shows that both SMAP and ASCAT retrievals improve the accuracy of soil moisture estimates. Especially, the SMAP single-sensor assimilation experiment shows the best performance with the increase of temporal anomaly correlation by ΔR ~ 0.05 for surface soil moisture and ΔR ~ 0.03 for root-zone soil moisture compared with the LSM simulation without satellite data assimilation. SMAP assimilation is more skillful than ASCAT assimilation primarily because of the greater skill of the assimilated SMAP retrievals compared to the ASCAT retrievals. The skill improvement also depends significantly on the region; the higher skill improvement in the western U.S. compared to the eastern U.S. is explained by the Kalman gain in the two experiments. Additionally, the regional skill differences in the single-sensor assimilation experiments are attributed to the number of assimilated observations. Finally, the soil moisture assimilation estimates provide more realistic land surface information than model-only simulations for the 2015 and the 2016 western U.S. droughts, suggesting the advantage of using satellite soil moisture retrievals in the current drought monitoring system.

SMAP↗
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 NTRSDate not supplied

Magnetic Suspension Wind Tunnel Reconstruction Using an Extended Kalman Filter Framework

A Kalman filter tool has been created for processing data from NASA Langley’s Magnetic Suspension and Balance System. The filter is formulated to estimate aerodynamic parameters of a model that is levitated magnetically in the test section of the wind tunnel. The Kalman filter tool is a modification of an existing code that has been in use for solving trajectory reconstruction problems and has been validated through previous use supporting many flight projects. Modifications to the code were implemented to add the capability to process data from the magnetic suspension wind tunnel. In particular, the main modifications were to the equations of motion to add models for magnetic and aerodynamic forces and moments. The code has been tested using simulation data to provide a known truth for verification.

Christopher D. Karlgaard↗
DOE OSTI2025

Covariance operator estimation: Sparsity, lengthscale, and ensemble Kalman filters

This paper investigates covariance operator estimation via thresholding. For Gaussian random fields with approximately sparse covariance operators, we establish non-asymptotic bounds on the estimation error in terms of the sparsity level of the covariance and the expected supremum of the field. We prove that thresholded estimators enjoy an exponential improvement in sample complexity compared with the standard sample covariance estimator if the field has a small correlation lengthscale. As an application of the theory, we study thresholded estimation of covariance operators within ensemble Kalman filters.

Covariance operator estimation↗