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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Real time prediction of marine vessel motions using Kalman filtering techniques

The present investigation is concerned with the prediction of the future behavior of a vessel within some confidence bounds at a specific instant of time, taking into account an interval of a few seconds. The ability to predict accurately the motions of a vessel can reduce significantly the probability of failure of operations in rough seas. The investigation was started as part of an effort to ensure safe landing of aircraft on relatively small vessels. However, the basic principles involved in the study are the same for any offshore operation, such as carbo transfer in the open sea, structure installation, and floating crane operation. The Kalman filter is a powerful tool for achieving the goals of the prediction procedure. Attention is given to a linear optimal predictor, the equations of motion of the vessel, the wave spectrum, rational approximation, the use of Kalman filter and predictor in an application for a ship, and the motions of a semisubmersible.

Triantafyllou, M. S.↗
NASA NTRS1983

Application of Kalman filters to robot calibration

This report explores new uses of Kalman filter theory in manufacturing systems (robots in particular). The Kalman filter allows the robot to read its sensors plus external sensors and learn from its experience. In effect, the robot is given primitive intelligence. The study, which is applicable to any type of powered kinematic linkage, focuses on the calibration of a manipulator.

Whitney, D. E.↗
NASA NTRS1983

Real time estimation of ship motions using Kalman filtering techniques

The estimation of the heave, pitch, roll, sway, and yaw motions of a DD-963 destroyer is studied, using Kalman filtering techniques, for application in VTOL aircraft landing. The governing equations are obtained from hydrodynamic considerations in the form of linear differential equations with frequency dependent coefficients. In addition, nonminimum phase characteristics are obtained due to the spatial integration of the water wave forces. The resulting transfer matrix function is irrational and nonminimum phase. The conditions for a finite-dimensional approximation are considered and the impact of the various parameters is assessed. A detailed numerical application for a DD-963 destroyer is presented and simulations of the estimations obtained from Kalman filters are discussed.

Triantafyllou, M. S.↗
NASA NTRS1985

Kalman-like estimation for static distributed systems Antenna shape from radiation measurements

This paper advances an approach to the determination of shape of static distributed systems. It also illustrates the application of the approach to the problems of surface diagnosis of large parabolic reflectors. The estimation methods developed combine in an optimal sense the information from an elliptic model of the structure and from measurements of the structural deflection and of the far-field pattern changes due to the structural deformation. The estimators have a predictor-corrector structure, quite similar to that of a Kalman filter. The system model is first used to obtain a predicted estimate. A correction term is then added to the prediction to obtain the final state estimate. The relative weighting between prediction and correction terms is determined by an estimator gain. As in a Kalman filter, the estimator gain can be expressed in terms of the state estimation error covariance.

Rodriguez, G.↗
NASA NTRS1986

Toward the application of the Kalman filter to regional open ocean modeling

A partial differential equation model is defined for ocean meteorological prediction and synoptic analysis. The Kalman filter used for data assimilation is described and applied to the one-dimensional linear barotropic quasi-geostrophic model with periodic and open boundary conditions. The model accounts for eddy scale dynamics in the ocean. The assumptions made in the forecast model are discussed, along with comparisons of the error variances expected with the filter and from an objective analysis method. The effectiveness of the Kalman filter is demonstrated and subsequent efforts to extend the filter to two dimensions are indicated.

Miller, R. N.↗
NASA NTRS1986

Numerical aspects of different Kalman filter implementations

A theoretical analysis is made of the error propagation due to numerical roundoff for four different Kalman filter implementations: the conventional Kalman filter, the square root covariance filter, the square root information filter, and the Chandrasekhar square root filter. An experimental analysis is performed to validate the new insights gained by the theoretical analysis.

Verhaegen, M.↗
NASA NTRS1987

Robustness of extended-Kalman-type observers

Robustness properties of extended-Kalman-type observers are investigated for linear plants when actuators and sensors have non-linearities or linear dynamics. Sufficient conditions for the stability of the estimation error are obtained for time-varying extended Kalman filters (EKF) for time-varying plants, and for constant-gain, exponentially weighted EKF for time-invariant plants. When the non-linearities are known only within bands of uncertainty, it is proved that the observer is non-divergent.

Joshi, S. M.↗
NASA NTRS1987

Investigation, development, and application of optimal output feedback theory. Volume 3: The relationship between dynamic compensators and observers and Kalman filters

Relationships between observers, Kalman Filters and dynamic compensators using feedforward control theory are investigated. In particular, the relationship, if any, between the dynamic compensator state and linear functions of a discrete plane state are investigated. It is shown that, in steady state, a dynamic compensator driven by the plant output can be expressed as the sum of two terms. The first term is a linear combination of the plant state. The second term depends on plant and measurement noise, and the plant control. Thus, the state of the dynamic compensator can be expressed as an estimator of the first term with additive error given by the second term. Conditions under which a dynamic compensator is a Kalman filter are presented, and reduced-order optimal estimaters are investigated.

Broussard, John R.↗
NASA NTRS1988

Relationships of earthquakes (and earthquake-associated mass movements) and polar motion as determined by Kalman filtered, Very-Long-Baseline-Interferometry

A Kalman filter was designed to yield optimal estimates of geophysical parameters from Very Long Baseline Interferometry (VLBI) group delay data. The geophysical parameters are the polar motion components, adjustments to nutation in obliquity and longitude, and a change in the length of day parameter. The VLBI clock (and clock rate) parameters and atmospheric zenith delay parameters are estimated simultaneously. Filter background is explained. The IRIS (International Radio Interferometric Surveying) VLBI data are Kalman filtered. The resulting polar motion estimates are examined. There are polar motion signatures at the times of three large earthquakes occurring in 1984 to 1986: Mexico, 19 September, 1985 (Magnitude M sub s = 8.1); Chile, 3 March, 1985 (M sub s = 7.8); and Taiwan, 14 November, 1986 (M sub s = 7.8). Breaks in polar motion occurring about 20 days after the earthquakes appear to correlate well with the onset of increased regional seismic activity and a return to more normal seismicity (respectively). While the contribution of these three earthquakes to polar motion excitations is small, the cumulative excitation due to earthquakes, or seismic phenomena over a Chandler wobble damping period may be significant. Mechanisms for polar motion excitation due to solid earth phenomena are examined. Excitation functions are computed, but the data spans are too short to draw conclusions based on these data.

Preisig, Joseph Richard Mark↗
NASA NTRS1988

Recursive least squares estimation and Kalman filtering by systolic arrays

One of the most promising new directions for high-throughput-rate problems is that based on systolic arrays. In this paper, using the matrix-decomposition approach, a systolic Kalman filter is formulated as a modified square-root information filter consisting of a whitening filter followed by a simple least-squares operation based on the systolic QR algorithm. By proper skewing of the input data, a fully pipelined time and measurement update systolic Kalman filter can be achieved with O(n squared) processing cells, resulting in a system throughput rate of O (n).

Chen, M. J.↗
NASA NTRS1988

New insights in the numerical reliability properties of existing Kalman filter implementations

The convergence properties of Kalman filter algorithms are investigated analytically. A theoretical error analysis is performed on four types of algorithms, as defined by Anderson and Moore (1979): (1) a conventional Kalman filter, (2) a square-root (SR) covariance filter, (3) the Chandrasekhar SR filter, and (4) an SR information filter. The derivations are given in detail, and numerical results for the flight-path reconstruction problem studied by Verhaegen (1987) are presented in tables and graphs. It is shown that error propagation in algorithms (1) and (2) is sensitive to the condition number of the innovation-signal covariance matrix and the spectral norm of the filter state-transition matrix, whereas other parameters are dominant in (3) and (4). Filter (2) is found to be the most reliable for the class of problems studied.

Verhaegen, M. H.↗
NASA NTRS1990

Discrete integration of continuous Kalman filtering equations for time invariant second-order structural systems

A general form for the first-order representation of the continuous second-order linear structural-dynamics equations is introduced to derive a corresponding form of first-order continuous Kalman filtering equations. Time integration of the resulting equations 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 Kalman filtering equations involving only symmetric sparse N x N solution matrices.

Park, K. C.↗
NASA NTRS1991

The History Of The Kalman Filter

Paper presents historical view of adaptation of Kalman filtering techniques to aerospace applications and eventually to fields as diverse as exploration for oil and control of powerplants. Describes scientific breakthroughs and reformulations that transformed Kalman filtering techniques into fundamental tool for analyzing and solving broad class of estimation problems.

Mcgee, Leonard A.↗
NASA NTRS1991

A simulation study of turbofan engine deterioration estimation using Kalman filtering techniques

Deterioration of engine components may cause off-normal engine operation. The result is an unecessary loss of performance, because the fixed schedules are designed to accommodate a wide range of engine health. These fixed control schedules may not be optimal for a deteriorated engine. This problem may be solved by including a measure of deterioration in determining the control variables. These engine deterioration parameters usually cannot be measured directly but can be estimated. A Kalman filter design is presented for estimating two performance parameters that account for engine deterioration: high and low pressure turbine delta efficiencies. The delta efficiency parameters model variations of the high and low pressure turbine efficiencies from nominal values. The filter has a design condition of Mach 0.90, 30,000 ft altitude, and 47 deg power level angle (PLA). It was evaluated using a nonlinear simulation of the F100 engine model derivative (EMD) engine, at the design Mach number and altitude over a PLA range of 43 to 55 deg. It was found that known high pressure turbine delta efficiencies of -2.5 percent and low pressure turbine delta efficiencies of -1.0 percent can be estimated with an accuracy of + or - 0.25 percent efficiency with a Kalman filter. If both the high and low pressure turbine are deteriorated, the delta efficiencies of -2.5 percent to both turbines can be estimated with the same accuracy.

Lambert, Heather H.↗
NASA NTRS1991

Simplification of the Kalman filter for meteorological data assimilation

The paper proposes a new statistical method of data assimilation that is based on a simplification of the Kalman filter equations. The forecast error covariance evolution is approximated simply by advecting the mass-error covariance field, deriving the remaining covariances geostrophically, and accounting for external model-error forcing only at the end of each forecast cycle. This greatly reduces the cost of computation of the forecast error covariance. In simulations with a linear, one-dimensional shallow-water model and data generated artificially, the performance of the simplified filter is compared with that of the Kalman filter and the optimal interpolation (OI) method. The simplified filter produces analyses that are nearly optimal, and represents a significant improvement over OI.

Dee, Dick P.↗
NASA NTRS1992

Estimation of Kalman filter gain from output residuals

This paper presents a procedure for estimating the Kalman filter gain from output residuals. The system state space model is assumed to be known, but the process and noise covariance are unknown. The proposed procedure consists of three basic steps. First, the output residuals are computed from the given model and a given set of input-output data. Second, a linear regression model for this part of the response is computed by a least squares solution. Third, the Kalman filter gain is then estimated from the coefficients of this model. Numerical results using experimental data are presented to illustrate the validity of the developed procedure.

Juang, Jer-Nan↗
NASA NTRS1991

Fusion techniques using distributed Kalman filtering for detecting changes in systems

A comparison is made of the performances of two detection strategies that are based on different data fusion techniques. The strategies detect changes in a linear system. One detection strategy involves combining the estimates and error covariance matrices of distributed Kalman filters, generating a residual from the used estimates, comparing this residual to a threshold, and making a decision. The other detection strategy involves a distributed decision process in which estimates from distributed Kalman filters are used to generate distributed residuals which are compared locally to a threshold. Local decisions are made and these decisions are then fused into a global decision. The performances of each of these detection schemes are compared, and it is concluded that better performance is achieved when local decisions are made and then fused into a global decision.

Belcastro, Celeste M.↗
NASA NTRS1993

Kalman-Filter Estimation Of Unmeasured Output Variables

Kalman-filter "observer" algorithm used in estimating unmeasured output variables of turbofan engine. Unmeasured output variables needed along with measured output variables as feedback data to characterize state of aircraft-and-engine system for control purposes. Subsystem or processor implementing Kalman-filter algorithm incorporated into adaptive digital control system, called "performance-seeking" control system, developed to compute engine-control settings to optimize performance of aircraft under given pseudo-steady-state cruise conditions. Basic concept of algorithm not restricted to aircraft-engine application and can be extended to internal-combustion engines in land vehicles.

Gilyard, Glenn B.↗