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Ibrahim Time-Domain modal vibration identification program (ITD) uses multiple free-decay responses of test structure directly in time domain to identify modal parameters of structure: natural frequencies, damping factors and damped mode shapes. ITD written in FORTRAN.
Identification sleeving slides or bunches during handling or vibration held in place by shrinkage tubing. Tubing slid over one end of cable and shrunk with hand-held hot-air gun.
A modeling method to calculate the intervals of uncertainty for parameters found by identification is described. The region of confidence and the general approach to the calculation of these intervals are discussed. The general subprograms for determination of dimensions are described. They provide the organizational charts for the subprograms, the tests carried out and the listings of the different subprograms.
Large space structures, system identification, model formulation, experimental design, model order and structure determination, parameter estimation, reduced order modeling, and closed loops are considered.
The Feature Identification and Location Experiment (FILE), which was flown on the second Space Shuttle flight to test a technique for real-time, autonomous classification of water, vegetation and bare land as well as clouds, snow and ice, senses earth radiation in spectral bands centered at 0.65 and 0.85 microns. The radiance ratio classification algorithm has successfully made automatic data selection decisions. A classification image obtained on the mission is providing data needed to evaluate the FILE algorithm and overall system performance.
Approximation results from linear semigroup theory are used to develop a general framework for convergence of approximation schemes in parameter estimation and optimal control problems for nonlinear partial differential equations. These ideas are used to establish theoretical convergence results for parameter identification using modal (eigenfunction) approximation techniques. Results from numerical investigations of these schemes for both hyperbolic and parabolic systems are given.
The Aerial V X-ray source 2A 1704+241 (= 4U 1700+24 = 3A 1703+241) is identified with the M3 II star HD 154791. The identification is based on a precise X-ray position determined by the HEAO 1 scanning modulation collimator and the Einstein Observatory imaging proportional counter, together with a spectrum measured by the International Ultraviolet Explorer. The ultraviolet spectrum shows strong emission of C IV 1550 A, N v 1238 A, and Mg II 2800 A, which is very unusual among M giants. This is the first X-ray detection of an M giant which has a completely normal optical spectrum. The X-ray luminosity reaches three orders of magnitude above the mean upper limit for the coronal X-ray flux from M giants. Although there is no direct evidence for a binary system, since radial velocity variations have not been observed, it is shown that a plausible neutron star binary model can be constructed.
The effect of a geometric nonlinearity on the Ibrahim Time Domain (ITD) modal data analysis technique has been studied using two analytically derived models and one laboratory model. Response data for the three models were analyzed by the ITD method. Indicators of nonlinear response were found which include harmonically related frequencies with repetitive mode shapes, clusters of frequencies in a narrow band around each harmonic, and variations in frequency with amplitude of oscillation. Also,for the cases studied, the presence of a nonlinearity has no detrimental effect on identifying linear responses. A potential for applying the algorithm to the identification of a certain class of nonlinear system was indicated.
A direct identification procedure is proposed to obtain the mass and stiffness matrices based on the test measured eigenvalues and eigenvectors. The method is based on the theory of matrix perturbation in which the correct mass and stiffness matrices are expanded in terms of analytical values plus a modification matrix. The simplicity of the procedure enables real time operation during the structural testing.
An approach for identifying the dynamics of large space structures is applied to a free-free beam. In this approach the system's order is determined on-line, along with mode shapes, using recursive lattice filters which provide a least square estimate of the measurement data. The mode shapes determined are orthonormal in the space of the measurements and, hence, are not the natural modes of the structure. To determine the natural modes of the structure, a method based on the fast Fourier transform is used on the outputs of the lattice filter. These natural modes are used to obtain the modal amplitude time series which provides the input data for an output error parameter identification scheme that identifies the ARMA parameters of the difference equation model of the modes. the approach is applied to both simulated and experimental data.
A recursive, extended Kalman-filter approach is applied to the identifiction of rotor damping levels of representative helicopter dynamic systems. The general formulation of the approach is presented in the context of a typically posed stochastic estimation problem, and the method is analytically applied to determining the damping levels of a coupled rotor-body system. The identified damping covergence characteristics are studied for sensitivity to both constant-coefficient and periodic-coefficient measurement models, process-noise covariance levels, and specified initial estimates of the rotor-system damping. A second application of the method to identifying the plant model for a highly damped, isolated flapping blade with a constant-coefficient state model (hover) and a periodic-coefficient state model (forward flight) is also investigated. The parameter-identification capability is evaluated for the effect of periodicity on the plant model coefficients and the influence of different measurement noise levels.
Airborne C- and L-band radar data acquired over a test site in western kansas were analyzed to determine corn-field identification accuracies obtainable using single-channel, multichannel, and multidate radar data. An automated pattern-recognition procedure was used to classify 144 fields into three categories: corn, pasture land, and bare soil (including wheat stubble and fallow). Corn fields were identified with accuracies ranging from 85 percent for single channel, single-date data to 100 percent for single-channel, multidate data. The effects of radar parameters such as frequency, polarization, and look angle as well as the effects of soil moisture on the classification accuracy are also presented.
An algorithm for processing flight-test data to provide state estimates and instrument calibrations for aerodynamic or hydrodynamic system identification by the equation error estimation method is developed and demonstrated on synthesized data. The extended-Kalman-filter algorithm employs a locally level, north-pointing frame of reference, accounts for rotating ellipsoidal earth effects, and estimates sensor bias, scale factors, wind components, and process noise levels by maximum-likelihood parameters. The method is found to be most effective with navigation quality inertial input data. The algorithm is applied to data from a six-degree-of-freedom F-4 aircraft simulation and shown to produce state estimates in good agreement with the simulation values.
The Aerodynamic Coefficient Identification Package (ACIP) is an instrument consisting of body mounted linear accelerometers, rate gyros, and angular accelerometers for measuring the Space Shuttle vehicular dynamics. The high rate recorded data are utilized for postflight aerodynamic coefficient extraction studies. Although consistent with pre-mission accuracies specified by the manufacturer, the ACIP data were found to contain detectable levels of systematic error, primarily bias, as well as scale factor, static misalignment, and temperature dependent errors. This paper summarizes the technique whereby the systematic ACIP error sources were detected, identified, and calibrated with the use of recorded dynamic data from the low rate, highly accurate Inertial Measurement Units.
The errors in the dynamic data output from the Aerodynamic Coefficient Identification Packages (ACIP) flown on Shuttle flights 1, 3, 4, and 5 were determined using the output from the Inertial Measurement Units (IMU). A weighted least-squares batch algorithm was empolyed. Using an averaging technique, signal detection was enhanced; this allowed improved calibration solutions. Global errors as large as 0.04 deg/sec for the ACIP gyros, 30 mg for linear accelerometers, and 0.5 deg/sec squared in the angular accelerometer channels were detected and removed with a combination is bias, scale factor, misalignment, and g-sensitive calibration constants. No attempt was made to minimize local ACIP dynamic data deviations representing sensed high-frequency vibration or instrument noise. Resulting 1sigma calibrated ACIP global accuracies were within 0.003 eg/sec, 1.0 mg, and 0.05 deg/sec squared for the gyros, linear accelerometers, and angular accelerometers, respectively.
A design approach for high speed normalization of digital signals was developed. A reciprocal look up table technique is employed, where a digital value is mapped to its reciprocal via a high speed memory. This reciprocal is then multiplied with an input signal to obtain the normalized result. Normalization improves considerably the accuracy of certain feature identification algorithms. By using the concept of pipelining the multispectral sensor data processing rate is limited only by the speed of the multiplier. The breadboard system was found to operate at an execution rate of five million normalizations per second. This design features high precision, a reduced hardware complexity, high flexibility, and expandability which are very important considerations for spaceborne applications. It also accomplishes a high speed normalization rate essential for real time data processing.
Statistical procedures were developed for large area assessments using both satellite and conventional data. Crop acreages, other ground cover indices, and measures of change were the principal characteristics of interest. These characteristics are capable of being estimated from samples collected possibly from several sources at varying times, with different levels of identification. Multiyear analysis techniques were extended to include partially identified samples; the best current year sampling design corresponding to a given sampling history was determined; weights reflecting the precision or confidence in each observation were identified and utilized, and the variation in estimates incorporating partially identified samples were quantified.
This paper seeks to develop the potential of lidar for the identification of the chemical composition of atmospheric aerosols. Available numerical computations suggest that aerosols can be identified by the wavelength dependence of aerosol optical properties. Since lidar can derive the volume backscatter coefficient as a function of wavelength, a multi-wavelength lidar system may be able to provide valuable information on the composition of aerosols. This research theoretically investigates the volume backscatter coefficients for the aerosol classes, sea-salts, and sulfates, as a function of wavelength. The results show that these aerosol compositions can be characterized and identified by their backscatter wavelength dependence. A method to utilize multi-wavelength lidar measurements to discriminate between compositionally different thin aerosol layers is discussed.