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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A proposed Kalman filter algorithm for estimation of unmeasured output variables for an F100 turbofan engine

To develop advanced control systems for optimizing aircraft engine performance, unmeasurable output variables must be estimated. The estimation has to be done in an uncertain environment and be adaptable to varying degrees of modeling errors and other variations in engine behavior over its operational life cycle. This paper represented an approach to estimate unmeasured output variables by explicitly modeling the effects of off-nominal engine behavior as biases on the measurable output variables. A state variable model accommodating off-nominal behavior is developed for the engine, and Kalman filter concepts are used to estimate the required variables. Results are presented from nonlinear engine simulation studies as well as the application of the estimation algorithm on actual flight data. The formulation presented has a wide range of application since it is not restricted or tailored to the particular application described.

Alag, Gurbux S.↗
NASA NTRS1991

Second-order discrete Kalman filtering equations for control-structure interaction simulations

A general form for the first-order representation of the continuous, second-order linear structural dynamics equations is introduced in order to derive a corresponding form of first-order Kalman filtering equations (KFE). Time integration of the resulting first-order KFE is carried out via a set of linear multistep integration formulas. It is shown that a judicious combined selection of computational paths and the undetermined matrices introduced in the general form of the first-order linear structural systems leads to a class of second-order discrete KFE involving only symmetric, N x N solution matrix.

Park, K. C.↗
NASA NTRS1990

Time scale algorithm: Definition of ensemble time and possible uses of the Kalman filter

The comparative study of two time scale algorithms, devised to satisfy different but related requirements, is presented. They are ALGOS(BIPM), producing the international reference TAI at the Bureau International des Poids et Mesures, and AT1(NIST), generating the real-time time scale AT1 at the National Institute of Standards and Technology. In each case, the time scale is a weighted average of clock readings, but the weight determination and the frequency prediction are different because they are adapted to different purposes. The possibility of using a mathematical tool, such as the Kalman filter, together with the definition of the time scale as a weighted average, is also analyzed. Results obtained by simulation are presented.

Tavella, Patrizia↗
NASA NTRS1990

Kalman filter based range estimation for autonomous navigation using imaging sensors

The ability to detect and locate obstacles using on-board sensors and modify the nominal trajectory is necessary for safe landing of an autonomous lander on Mars. This paper examines some of the issues in the location of objects using a sequence of images from a passive sensor, and describes a Kalman filter approach to improve the range estimation to obstacles. The filter is also used to track features in the images leading to a significant reduction of search effort in the feature extraction step of the algorithm. The lack of suitable flight imagery data presents a problem in the verification of concepts for obstacle detection. An experiment is designed to acquire a sequence of images along with sensor motion parameters and the range estimation results using this imagery are presented.

Sridhar, B.↗
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 NTRS1991

Mass property identification - A comparison study between extended Kalman filter and neuro-filter approaches

Two algorithms, extended Kalman filter and neuro-filter, are formulated to perform mass property identification for the Space Station Freedom. Control moment gyros that are part of the Station's basic momentum management system are chosen to provide input excitation in the form of applied torques. These torques together with the measured angular body rate responses are supplied to the filters. From these data, both algorithms are shown to accurately identify the station mass properties when excitation levels are high and balanced between axes. The neuro-filter, however, is shown to be more robust and to perform well even with weakly persistent, unbalanced signals contaminated with noise.

Lam, Quang↗
NASA NTRS1991

An extended Kalman filter for spinning spacecraft attitude estimation

An extended Kalman filter for real-time ground attitude estimation of a gyro-less spinning spacecraft was developed and tested. The filter state vector includes the angular momentum direction, phase angle, inertial nutation angle, and inertial and body nutation rates. The filter solves for the nutating three-axis attitude and accounts for effects due to principle axes offset from the body axes. The attitude is propagated using the kinematics of a rigid body symmetric about the principle spin axis; disturbance torques are assumed to be small. Filter updates consist only of the measured angles between celestial objects (Sun, Earth, etc.) and the nominal spin axis, and the times these angles were measured. Both simulated data and real data from the Dynamics Explorer -A (DE-A) spacecraft were used to test the filter; the results are presented. Convergence was achieved rapidly from a wide range of a priori state estimates, and sub-degree accuracy was attained. Systematic errors affecting the solution accuracy are discussed, as are the results of an attempt to solve for sensor measurement angle biases in the state vector.

Baker, David F.↗
NASA NTRS1991

Restoration of color images by multichannel Kalman filtering

A Kalman filter for optimal restoration of multichannel images is presented. This filter is derived using a multichannel semicausal image model that includes between-channel degradation. Both stationary and nonstationary image models are developed. This filter is implemented in the Fourier domain and computation is reduced from O(Lambda3N3M4) to O(Lambda3N3M2) for an M x M N-channel image with degradation length Lambda. Color (red, green, and blue (RGB)) images are used as examples of multichannel images, and restoration in the RGB and YIQ domains is investigated. Simulations are presented in which the effectiveness of this filter is tested for different types of degradation and different image model estimates.

Galatsanos, Nikolas P.↗
NASA NTRS1989

The application of the reduced order model Kalman filter to motion estimation of degraded image sequences

Motion estimation is a field of great interest because of its many applications in areas such as robotics and image coding. The optic flow method is one such scheme which, although fairly accurate, is prone to error in the presence of noise. This thesis describes the use of the reduced order model Kalman filter (ROMKF) in reducing errors in displacement estimation due to degradation of the sequence. The implementation of filtering and motion estimation algorithms on the SUN workstation is also discussed. Results from preliminary testing were used to determine the degrees of freedom available for the ROMKF in the SUN software. The tests indicated that increasing the state to the left leads to slight improvement over the minimum state case. Therefore, the software uses the minimum model, with the option of adding states to the left only. The ROMKF was then used in conjunction with a hierarchical pel recursive motion estimation algorithm. Applying the ROMKF to the degraded displacements themselves generally yielded slight improvements in cases with noise degradation and noise plus blur. Filtering the images of the degraded sequence prior to motion estimation was less effective in these cases. Both methods performed badly in the case of blur alone, resulting in increased displacement errors. This is thought to be due in part to filter artifacts. Some improvements were obtained by varying the filter parameters when filtering the displacements directly. This result suggests that further study in varying filter parameters may lead to better results. The results of this thesis indicate that the ROMKF can play a part in reducing motion estimation errors from degraded sequences. However, more work needs to be done before the use of the ROMKF can be a practical solution.

Simpson, Elizabeth C.↗
NASA NTRS1991

Discrete Kalman filtering equations of second-order form for control-structure interaction simulations

A second-order form of discrete Kalman filtering equations is proposed as a candidate state estimator for efficient simulations of control-structure interactions in coupled physical coordinate configurations as opposed to decoupled modal coordinates. The resulting matrix equation of the present state estimator consists of the same symmetric, sparse N x N coupled matrices of the governing structural dynamics equations as opposed to unsymmetric 2N x 2N state space-based estimators. Thus, in addition to substantial computational efficiency improvement, the present estimator can be applied to control-structure design optimization for which the physical coordinates associated with the mass, damping and stiffness matrices of the structure are needed instead of modal coordinates.

Park, K. C.↗
NASA NTRS1993

Kalman filtering applied to real-time monitoring of apogee maneuvers

Part of the Space Mathematics Division in CNES, the Flight Dynamics Center provides attitude and orbit determinations and maneuvers during the Launch and Early Operation Phase (LEOP) of geostationary satellites. Orbit determination is based on a Kalman filter method; when the 2 GHz CNES/NASA network is used, Doppler measurements are available and allow orbit determination during the apogee maneuvers. This method was used for TELE-X and TDF 2 LEOP (3-axis controlled satellites) and also for TELECOM 2 and HISPASAT (spun satellites): it enables us to follow the evolution of the maneuver and gives out a quite accurate estimation of the reached orbit. In this paper, we briefly describe the dynamic models of the orbit evolution in both cases, '3-axis' and 'inertial' thrust. Then, we present the results obtained for each case. Afterwards, we present some cases to show the robustness of the filter.

Deboer, Frederic↗
NASA NTRS1994

Suboptimal schemes for atmospheric data assimilation based on the Kalman filter

This work is directed toward approximating the evolution of forecast error covariances for data assimilation. The performance of different algorithms based on simplification of the standard Kalman filter (KF) is studied. These are suboptimal schemes (SOSs) when compared to the KF, which is optimal for linear problems with known statistics. The SOSs considered here are several versions of optimal interpolation (OI), a scheme for height error variance advection, and a simplified KF in which the full height error covariance is advected. To employ a methodology for exact comparison among these schemes, a linear environment is maintained, in which a beta-plane shallow-water model linearized about a constant zonal flow is chosen for the test-bed dynamics. The results show that constructing dynamically balanced forecast error covariances rather than using conventional geostrophically balanced ones is essential for successful performance of any SOS. A posteriori initialization of SOSs to compensate for model - data imbalance sometimes results in poor performance. Instead, properly constructed dynamically balanced forecast error covariances eliminate the need for initialization. When the SOSs studied here make use of dynamically balanced forecast error covariances, the difference among their performances progresses naturally from conventional OI to the KF. In fact, the results suggest that even modest enhancements of OI, such as including an approximate dynamical equation for height error variances while leaving height error correlation structure homogeneous, go a long way toward achieving the performance of the KF, provided that dynamically balanced cross-covariances are constructed and that model errors are accounted for properly. The results indicate that such enhancements are necessary if unconventional data are to have a positive impact.

Todling, Ricardo↗
NASA NTRS1995

An approximate Kalman filter for ocean data assimilation: An example with an idealized Gulf Stream model

A practical method of data assimilation for use with large, nonlinear, ocean general circulation models is explored. A Kalman filter based on approximation of the state error covariance matrix is presented, employing a reduction of the effective model dimension, the error's asymptotic steady state limit, and a time-invariant linearization of the dynamic model for the error integration. The approximations lead to dramatic computational savings in applying estimation theory to large complex systems. We examine the utility of the approximate filter in assimilating different measurement types using a twin experiment of an idealized Gulf Stream. A nonlinear primitive equation model of an unstable east-west jet is studied with a state dimension exceeding 170,000 elements. Assimilation of various pseudomeasurements are examined, including velocity, density, and volume transport at localized arrays and realistic distributions of satellite altimetry and acoustic tomography observations. Results are compared in terms of their effects on the accuracies of the estimation. The approximate filter is shown to outperform an empirical nudging scheme used in a previous study. The examples demonstrate that useful approximate estimation errors can be computed in a practical manner for general circulation models.

Fukumori, Ichiro↗
NASA NTRS1996

Inertial Head-Tracker Sensor Fusion by a Complementary Separate-Bias Kalman Filter

Current virtual environment and teleoperator applications are hampered by the need for an accurate, quick-responding head-tracking system with a large working volume. Gyroscopic orientation sensors can overcome problems with jitter, latency, interference, line-of-sight obscurations, and limited range, but suffer from slow drift. Gravimetric inclinometers can detect attitude without drifting, but are slow and sensitive to transverse accelerations. This paper describes the design of a Kalman filter to integrate the data from these two types of sensors in order to achieve the excellent dynamic response of an inertial system without drift, and without the acceleration sensitivity of inclinometers.

Foxlin, Eric↗
NASA NTRS1996

Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter

Current virtual environment and teleoperator applications are hampered by the need for an accurate, quick responding head-tracking system with a large working volume. Gyroscopic orientation sensors can overcome problems with jitter, latency, interference, line-of-sight obscurations, and limited range, but suffer from slow drift. Gravimetric inclinometers can detect attitude without drifting, but are slow and sensitive to transverse accelerations. This paper describes the design of a Kalman filter to integrate the data from these two types of sensors in order to achieve the excellent dynamic response of an inertial system without drift, and without the acceleration sensitivity of inclinometers.

Foxlin, Eric↗
NASA NTRS1996

Relating the Hadamard Variance to MCS Kalman Filter Clock Estimation

The Global Positioning System (GPS) Master Control Station (MCS) currently makes significant use of the Allan Variance. This two-sample variance equation has proven excellent as a handy, understandable tool, both for time domain analysis of GPS cesium frequency standards, and for fine tuning the MCS's state estimation of these atomic clocks. The Allan Variance does not explicitly converge for the nose types of alpha less than or equal to minus 3 and can be greatly affected by frequency drift. Because GPS rubidium frequency standards exhibit non-trivial aging and aging noise characteristics, the basic Allan Variance analysis must be augmented in order to (a) compensate for a dynamic frequency drift, and (b) characterize two additional noise types, specifically alpha = minus 3, and alpha = minus 4. As the GPS program progresses, we will utilize a larger percentage of rubidium frequency standards than ever before. Hence, GPS rubidium clock characterization will require more attention than ever before. The three sample variance, commonly referred to as a renormalized Hadamard Variance, is unaffected by linear frequency drift, converges for alpha is greater than minus 5, and thus has utility for modeling noise in GPS rubidium frequency standards. This paper demonstrates the potential of Hadamard Variance analysis in GPS operations, and presents an equation that relates the Hadamard Variance to the MCS's Kalman filter process noises.

Hutsell, Steven T.↗
NASA NTRS1996

ILIAD Testing; and a Kalman Filter for 3-D Pose Estimation

This report presents the results of a two-part project. The first part presents results of performance assessment tests on an Internet Library Information Assembly Data Base (ILIAD). It was found that ILLAD performed best when queries were short (one-to-three keywords), and were made up of rare, unambiguous words. In such cases as many as 64% of the typically 25 returned documents were found to be relevant. It was also found that a query format that was not so rigid with respect to spelling errors and punctuation marks would be more user-friendly. The second part of the report shows the design of a Kalman Filter for estimating motion parameters of a three dimensional object from sequences of noisy data derived from two-dimensional pictures. Given six measured deviation values represendng X, Y, Z, pitch, yaw, and roll, twelve parameters were estimated comprising the six deviations and their time rate of change. Values for the state transiton matrix, the observation matrix, the system noise covariance matrix, and the observation noise covariance matrix were determined. A simple way of initilizing the error covariance matrix was pointed out.

Richardson, A. O.↗
NASA NTRS1997

Estimating Attitude, Trajectory, and Gyro Biases in an Extended Kalman Filter using Earth Magnetic Field Data from the Rossi X-Ray Timing Explorer

Traditionally satellite attitude and trajectory have been estimated with completely separate systems, using different measurement data. The estimation of both trajectory and attitude for low earth orbit satellites has been successfully demonstrated in ground software using magnetometer and gyroscope data. Since the earth's magnetic field is a function of time and position, and since time is known quite precisely, the differences between the computed and measured magnetic field components, as measured by the magnetometers throughout the entire spacecraft orbit, are a function of both the spacecraft trajectory and attitude errors. Therefore, these errors can be used to estimate both trajectory and attitude. This work further tests the single augmented Extended Kalman Filter (EKF) which simultaneously and autonomously estimates spacecraft trajectory and attitude with data from the Rossi X-Ray Timing Explorer (RXTE) magnetometer and gyro-measured body rates. In addition, gyro biases are added to the state and the filter's ability to estimate them is presented.

Deutschmann, Julie↗