Application of parameter estimation to highly unstable aircraft
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Maine, Richard E.↗
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A spline-based parameter estimation technique for static models of elastic structures
The problem of identifying the spatially varying coefficient of elasticity using an observed solution to the forward problem is considered. Under appropriate conditions this problem can be treated as a first order hyperbolic equation in the unknown coefficient. Some continuous dependence results are developed for this problem and a spline-based technique is proposed for approximating the unknown coefficient, based on these results. The convergence of the numerical scheme is established and error estimates obtained.
Dutt, P.↗
Parameter estimation for flight vehicles
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Iliff, Kenneth W.↗
Parameter estimation problems for distributed systems using a multigrid method
The problem of estimating spatially varying coefficients of partial differential equations is considered from observation of the solution and of the right hand side of the equation. It is assumed that the observations are distributed in the domain and that enough observations are given. A method of discretization and an efficient multigrid method for solving the resulting discrete systems are described. Numerical results are presented for estimation of coefficients in an elliptic and a parabolic partial differential equation.
Ta'asan, Shlomo↗
Attitude determination and parameter estimation using vector observations - Application
This paper presents tests of a new method for the simultaneous estimation of spacecraft attitude and sensor biases, based on a quaternion estimation algorithm minimizing Wahba's loss function. The new method is compared with a conventional batch least-squares differential correction algorithm. The estimates are based on data from strapdown gyros and star trackers, simulated with varying levels of Gaussian noise for both inertially-fixed and earth-pointing attitudes. Both algorithms solve for the spacecraft attitude and the gyro drift rate biases. In the majority of tests performed, the two methods converge to the same estimates in the same number of iterations, but the new algorithm requires about 60 percent more computational effort. Some cases were found in which the new method converges in fewer iterations than the differential correction, and some for which the differential correction requires fewer iterations.
Markley, F. Landis↗
Phase noise effects on turbulent weather radar spectrum parameter estimation
Accurate weather spectrum moment estimation is important in the use of weather radar for hazardous windshear detection. The effect of the stable local oscillator (STALO) instability (jitter) on the spectrum moment estimation algorithm is investigated. Uncertainty in the stable local oscillator will affect both the transmitted signal and the received signal since the STALO provides transmitted and reference carriers. The proposed approach models STALO phase jitter as it affects the complex autocorrelation of the radar return. The results can therefore by interpreted in terms of any source of system phase jitter for which the model is appropriate and, in particular, may be considered as a cumulative effect of all radar system sources.
Lee, Jonggil↗
Application of a novel multi-stage signal parameter estimator to high dynamic GPS receivers
The performance of a novel multistage estimator when applied to the estimation of the position, velocity, and acceleration of high dynamic Global Positioning System (GPS) receivers is discussed. For the present application, a two-stage specialization of the more general estimation scheme is considered, wherein the first-stage algorithm is selected to be a modified least-squares algorithm operating upon the differential signal model and referred to as differential least-squares (DLS) and the second stage is simply an extended Kalman filter (EKF). In terms of the threshold on received carrier power-to-noise power spectral density ratio (CNR), when compared to the single-stage EKF algorithm, the DLS-EKF algorithm is about 1.5-2.0 dB better in terms of threshold and outperforms the crossproduct AFC (automatic frequency control) loop by 2-5 dB. For the case when data modulation is present, the proposed scheme provides an improvement of about 6 dB in terms of CNR compared to an earlier approximate MLE (maximum likelihood estimation) scheme. There are also very significant improvements in terms of other performance measures.
Kumar, R.↗
Observation model and parameter partials for the JPL VLBI parameter estimation software MODEST/1991
A revision is presented of MASTERFIT-1987, which it supersedes. Changes during 1988 to 1991 included introduction of the octupole component of solid Earth tides, the NUVEL tectonic motion model, partial derivatives for the precession constant and source position rates, the option to correct for source structure, a refined model for antenna offsets, modeling the unique antenna at Richmond, FL, improved nutation series due to Zhu, Groten, and Reigber, and reintroduction of the old (Woolard) nutation series for simulation purposes. Text describing the relativistic transformations and gravitational contributions to the delay model was also revised in order to reflect the computer code more faithfully.
Sovers, O. J.↗
Distributed parameter estimation for NASA Mini-Mast truss using Timoshenko beam model
A more accurate Timoshenko beam model is used to characterize the bending behavior of the truss. A maximum likelihood estimator for the Timoshenko beam model has been formulated. A closed-form solution of the Timoshenko beam equation, for a uniform cantilevered beam with two concentrated masses, is derived so that the procedure for the estimation of modal characteristics is much improved. The updated model to the NASA Mini-Mast test data is demonstrated.
Shen, Ji-Yao↗
Development of an on-line parameter estimation system using the discrete modal filter
Ongoing development of an automated system for monitoring the frequency and damping of time varying structural systems is discussed. A spatial filtering technique called the discrete modal filter is used to uncouple multiple response measurements into modal coordinate responses. Since the modal coordinate responses are the responses of single-degree-of-freedom systems, the frequency and damping may be accurately estimated with short data records. An on-line monitoring system is discussed which may be useful in a variety of applications. The focus in this paper is on the application to aircraft flight flutter testing.
Shelley, S. J.↗
Parameter Estimation in Astronomy with Poisson-Distributed Data: The (CHI)2(gamma) Statistic - 1
Applying the standard weighted mean formula, [Sigma (sub i)n(sub i)ssigma(sub i, sup -2)], to determine the weighted mean of data, n(sub i), drawn from a Poisson distribution, will, on average, underestimate the true mean by approx. 1 for all true mean values larger than approx.3 when the common assumption is made that the error of the i th observation is sigma(sub i) = max square root of n(sub i), 1).This small, but statistically significant offset, explains the long-known observation that chi-square minimization techniques which use the modified Neyman'chi(sub 2) statistic, chi(sup 2, sub N) equivalent Sigma(sub i)((n(sub i) - y(sub i)(exp 2)) / max(n(sub i), 1), to compare Poisson - distributed data with model values, y(sub i), will typically predict a total number of counts that underestimates the true total by about 1 count per bin. Based on my finding that weighted mean of data drawn from a Poisson distribution can be determined using the formula [Sigma(sub i)[n(sub i) + min(n(sub i), 1)](n(sub i) + 1)(exp -1)] / [Sigma(sub i)(n(sub i) + 1)(exp -1))], I propose that a new chi(sub 2) statistic, chi(sup 2, sub gamma) equivalent, should always be used to analyze Poisson- distributed data in preference to the modified Neyman's chi(exp 2) statistic. I demonstrated the power and usefulness of,chi(sub gamma, sup 2) minimization by using two statistical fitting techniques and five chi(exp 2) statistics to analyze simulated X-ray power - low 15 - channel spectra with large and small counts per bin. I show that chi(sub gamma, sup 2) minimization with the Levenberg - Marquardt or Powell's method can produce excellent results (mean slope errors approx. less than 3%) with spectra having as few as 25 total counts.
Mighell, Kenneth J.↗
Improved Speech Coding Based on Open-Loop Parameter Estimation
A nonlinear optimization algorithm for linear predictive speech coding was developed early that not only optimizes the linear model coefficients for the open loop predictor, but does the optimization including the effects of quantization of the transmitted residual. It also simultaneously optimizes the quantization levels used for each speech segment. In this paper, we present an improved method for initialization of this nonlinear algorithm, and demonstrate substantial improvements in performance. In addition, the new procedure produces monotonically improving speech quality with increasing numbers of bits used in the transmitted error residual. Examples of speech encoding and decoding are given for 8 speech segments and signal to noise levels as high as 47 dB are produced. As in typical linear predictive coding, the optimization is done on the open loop speech analysis model. Here we demonstrate that minimizing the error of the closed loop speech reconstruction, instead of the simpler open loop optimization, is likely to produce negligible improvement in speech quality. The examples suggest that the algorithm here is close to giving the best performance obtainable from a linear model, for the chosen order with the chosen number of bits for the codebook.
Juang, Jer-Nan↗
A Catchment-Based Approach to Modeling Land Surface Processes in a GCM: Parameter Estimation and Model Demonstration - Part 2
The viability of a new catchment-based land surface model (LSM) developed for use with general circulation models is demonstrated. First, simple empirical functions -- tractable enough for operational use in the LSM -- are established that faithfully capture the control of topography on the subgrid variability of soil moisture and the surface water budget, as predicted by theory. Next, the full LSM is evaluated offline. Using forcing and validation datasets developed for PILPS Phase 2c, the minimally calibrated model is shown to reproduce observed evaporation and runoff fluxes successfully in the Red-Arkansas River Basin. A complementary idealized study that employs the range of topographic variability seen over North America demonstrates that the simulated surface water budget does vary strongly with topography, which can, by itself, induce variations in annual evaporation as high as 20%.
Ducharne, Agnes↗
Multiple Input Design for Real-Time Parameter Estimation in the Frequency Domain
A method for designing multiple inputs for real-time dynamic system identification in the frequency domain was developed and demonstrated. The designed inputs are mutually orthogonal in both the time and frequency domains, with reduced peak factors to provide good information content for relatively small amplitude excursions. The inputs are designed for selected frequency ranges, and therefore do not require a priori models. The experiment design approach was applied to identify linear dynamic models for the F-15 ACTIVE aircraft, which has multiple control effectors.
Morelli, Eugene↗
Land parameter estimation using satellite microwave radiometry: retrieval issues
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soil moisture microwave radiometry hydrology↗
An adaptive threshold detector and channel parameter estimator for deep space optical communications
This paper presents a method for optimal adaptive setting of ulse-position-modulation pulse detection thresholds, which minimizes the total probability of error for the dynamically fading optical fee space channel.
pulse position modulation optical communications a↗
Joint Model and Phase Parameter Estimation in Sparse Synthetic Aperture Radar Interferometric Measurements
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interferometry isolated interferometric geodetic m↗
Surveillance system and method having parameter estimation and operating mode partitioning
A system and method for monitoring an apparatus or process asset including creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto; acquiring a set of observed signal data values from the asset; determining an operating mode of the asset for the set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a set of estimated signal data values from the selected process submodel for the determined operating mode; and determining asset status as a function of the calculated set of estimated signal data values for providing asset surveillance and/or control.
Bickford, Randall L.↗