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At least 883 records · Page 49

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NASA NTRS1978

Can low-cost VOR and Omega receivers suffice for RNAV - A new computer-based navigation technique

It is shown that although RNAV is particularly valuable for the personal transportation segment of general aviation, it has not gained complete acceptance. This is due, in part, to its high cost and the necessary special-handling air traffic control. VOR/DME RNAV calculations are ideally suited for analog computers, and the use of microprocessor technology has been suggested for reducing RNAV costs. Three navigation systems, VOR, Omega, and DR, are compared for common navigational difficulties, such as station geometry, siting errors, ground disturbances, and terminal area coverage. The Kalman filtering technique is described with reference to the disadvantages when using a system including standard microprocessors. An integrated navigation system, using input data from various low-cost sensor systems, is presented and current simulation studies are noted.

Hollaar, L. A.↗

NASA NTRS1979

Orbit/attitude estimation with Landsat-1 and -2 landmark data

The accuracy of spacecraft orbit and attitude determination is investigated using Landsat-1 and -2 picture data. The data consists of ground control points, of known geodetic coordinates, which are identified on a series of Landsat pictures. Both sequential (extended Kalman filter) and batch (nonlinear Bayesian weighted least-squares) estimation techniques have been implemented in two software systems. Discussion of these systems includes a system overview, mathematical formulation, and sample numerical results. Particular emphasis is given to the application of a spectral analysis technique to derive spacecraft attitude models.

Hall, D. L.↗

NASA NTRS1979

A subroutine package for discrete estimation problems

In this paper we describe a well documented, compactly coded, storage efficient, thoroughly tested, and easy to use set of FORTRAN IV subroutines for use in Kalman filter or least-squares applications. The package contains both the UDU covariance factorization and the square root information filter algorithms developed at the Jet Propulsion Laboratory. Numerical reliability of the algorithms is a key feature of the package.

Bierman, G. J.↗

NASA NTRS1979

Applications of estimation theory to inverse problems in meteorology

The paper applies iterated and non-iterated extended Kalman filters to solve two practical meteorological inversion problems. Passive microwave satellite soundings are used to infer vertical temperature profiles and cloud parameters. In both cases it is shown that improvements can be obtained over presently used techniques. Finally, the results suggest that modern multivariate nonlinear recursive estimation techniques based in a Bayesian methodology can be a valuable tool in the area of remote sounding of atmospheric parameters.

Gustafson, D. E.↗

NASA NTRS1980

Linear stochastic optimal control and estimation problem

Problem involves design of controls for linear time-invariant system disturbed by white noise. Solution is Kalman filter coupled through set of optimal regulator gains to produce desired control signal. Key to solution is solving matrix Riccati differential equation. LSOCE effectively solves problem for wide range of practical applications. Program is written in FORTRAN IV for batch execution and has been implemented on IBM 360.

Geyser, L. C.↗

NASA NTRS1978

Quiet Clean Short-haul Experimental Engine (QCSEE) over-the-wing engine and control simulation results

A hybrid-computer simulation of the over the wing turbofan engine was constructed to develop the dynamic design of the control. This engine and control system includes a full authority digital electronic control using compressor stator reset to achieve fast thrust response and a modified Kalman filter to correct for sensor failures. Fast thrust response for powered-lift operations and accurate, fast responding, steady state control of the engine is provided. Simulation results for throttle bursts from 62 to 100 percent takeoff thrust predict that the engine will accelerate from 62 to 95 percent takeoff thrust in one second.

Source record↗

NASA NTRS1979

Identification evaluation methods

Methods for airplane parameter estimation, the equation error method, output error method, and two advanced methods are presented and their basic properties described. The advanced methods include the maximum likelihood and extended Kalman filter method. For a better understanding of the estimation techniques a first-order scalar differential equation is used as a model of the system under test. Application of the methods to a general multivariable linear system is briefly outlined. A note on the parameter estimation in the frequency domain is also presented. Numerical examples along with the comparison of results from various methods are given.

Klein, V.↗

NASA NTRS1980

Maximum likelihood method for estimating airplane stability and control parameters from flight data in frequency domain

A frequency domain maximum likelihood method is developed for the estimation of airplane stability and control parameters from measured data. The model of an airplane is represented by a discrete-type steady state Kalman filter with time variables replaced by their Fourier series expansions. The likelihood function of innovations is formulated, and by its maximization with respect to unknown parameters the estimation algorithm is obtained. This algorithm is then simplified to the output error estimation method with the data in the form of transformed time histories, frequency response curves, or spectral and cross-spectral densities. The development is followed by a discussion on the equivalence of the cost function in the time and frequency domains, and on advantages and disadvantages of the frequency domain approach. The algorithm developed is applied in four examples to the estimation of longitudinal parameters of a general aviation airplane using computer generated and measured data in turbulent and still air. The cost functions in the time and frequency domains are shown to be equivalent; therefore, both approaches are complementary and not contradictory. Despite some computational advantages of parameter estimation in the frequency domain, this approach is limited to linear equations of motion with constant coefficients.

Klein, V.↗

NASA NTRS1980

A study of the effects of state transition matrix approximations

The effects of using an approximate state transition matrix in orbit estimation are investigated. The approximate state transition matrix results when higher order geopotential terms in the equations of motion are ignored in the formation of the variational equations. Two methods of orbit estimation are considered: the differential correction procedure (DC) and the extended Kalman filter (EKF). The system used for the study is the Research & Development version of the Goddard Trajectory Determination System. The effects of the approximation are analyzed on a number of orbits. These orbits of various inclinations and semimajor axes. Other parameters studied include geopotential models and DC arc length.

May, J. A.↗

NASA NTRS1979

A user oriented microcomputer facility for designing linear quadratic Gaussian feedback compensators

The paper describes a laboratory design facility for digital microprocessor implementation of Linear-Quadratic-Gaussian feedback compensators. Outputs from user interactive programs for solving infinite time horizon LQ regulator and Kalman filter problems are conditioned for implementation on a laboratory microcomputer system. The software consists of two parts: (1) an off-line high-level program for solving the LQ Ricatti equations and generating associated feedback and filter gains, and (2) a cross compiler/macro assembler which generates object code for the target microprocessor system. Application to the control of a two dimensional inverted pendulum and expanding the design/prototyping system to other target machine architectures are discussed.

Houpt, P. K.↗

NASA NTRS1979

Optimal estimator model for human spatial orientation

A model is being developed to predict pilot dynamic spatial orientation in response to multisensory stimuli. Motion stimuli are first processed by dynamic models of the visual, vestibular, tactile, and proprioceptive sensors. Central nervous system function is then modeled as a steady-state Kalman filter which blends information from the various sensors to form an estimate of spatial orientation. Where necessary, this linear central estimator has been augmented with nonlinear elements to reflect more accurately some highly nonlinear human response characteristics. Computer implementation of the model has shown agreement with several important qualitative characteristics of human spatial orientation, and it is felt that with further modification and additional experimental data the model can be improved and extended. Possible means are described for extending the model to better represent the active pilot with varying skill and work load levels.

Borah, J.↗

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 NTRS1980

Determination of instrumentation errors from measured data using maximum likelihood method

The maximum likelihood method is used for estimation of unknown initial conditions, constant bias and scale factor errors in measured flight data. The model for the system to be identified consists of the airplane six-degree-of-freedom kinematic equations, and the output equations specifying the measured variables. The estimation problem is formulated in a general way and then, for practical use, simplified by ignoring the effect of process noise. The algorithm developed is first applied to computer generated data having different levels of process noise for the demonstration of the robustness of the method. Then the real flight data are analyzed and the results compared with those obtained by the extended Kalman filter algorithm.

Keskar, D. A.↗

NASA NTRS1980

Alternative approach to multisensor navigation

An alternative to the Kalman filtering technique, based on logical rather than numerical techniques, is presented. It can be implemented on one or more microprocessors (with a fixed communications protocol between the individual microprocessors); requires only limited preprocessing of the input data; can have its constituent subsystem changed without major revision to the combiner as new or better navigation techniques, such as the global positioning system (GPS) or Loran-C, become available; and tolerates a wide variety of sensor, hardware, and software errors. The basic structure of the method and results of simulations of its performance in the presence of errors common to low-cost sensors (such as VOR or basic Omega) are presented, along with a discussion of possible extensions.

Cannon, M. J.↗

NASA NTRS1980

Semiannalytical satellite theory and sequential estimation

Kalman filtering techniques are combined with a semianalytical orbit generator to develop a sequential orbit determination algorithm. The algorithm is investigated for computational efficiency, accuracy, and radius of convergence by comparison with truth ephemerides and a Cowell special perturbations filters (GTDS). Test cases relevant to satellite navigation are examined.

Taylor, S. P.↗

NASA NTRS1980

Development of advanced techniques for rotorcraft state estimation and parameter identification

An integrated methodology for rotorcraft system identification consists of rotorcraft mathematical modeling, three distinct data processing steps, and a technique for designing inputs to improve the identifiability of the data. These elements are as follows: (1) a Kalman filter smoother algorithm which estimates states and sensor errors from error corrupted data. Gust time histories and statistics may also be estimated; (2) a model structure estimation algorithm for isolating a model which adequately explains the data; (3) a maximum likelihood algorithm for estimating the parameters and estimates for the variance of these estimates; and (4) an input design algorithm, based on a maximum likelihood approach, which provides inputs to improve the accuracy of parameter estimates. Each step is discussed with examples to both flight and simulated data cases.

Hall, W. E., Jr.↗

NASA NTRS1981

F-8C adaptive control law refinement and software development

An explicit adaptive control algorithm based on maximum likelihood estimation of parameters was designed. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm was implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer, surface position) are telemetered to a ground computer which sends new gain values to an on-board system. Ground test data and flight records were used to establish design values of noise statistics and to verify the ground-based adaptive software.

Hartmann, G. L.↗

NASA NTRS1981

Navstar Global Positioning System (GPS) clock program: Present and future

Global Positioning System (GPS) program status are discussed and plans for ensuring the long term continuation of the program are presented. Performance of GPS clocks is presented in terms of on orbit data as portrayed by GPS master control station kalman filter processing. The GPS Clock reliability program is reviewed in depth and future plans fo the overall clock program are published.

Tennant, D. M.↗