Minimum variance technique for the analysis of ionospheric data acquired in satellite retarding potential analyzer experiments.
Minimum variance technique for analysis of ionospheric data acquired by satellite retarding potential analyzer
Hyman, E.↗
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Minimum variance system identification with application to digital adaptive flight control
A new on-line minimum variance filter for the identification of systems with additive and multiplicative noise is described which embodies both accuracy and computational efficiency. The resulting filter is shown to use both the covariance of the parameter vector itself and the covariance of the error in identification. A bias reduction scheme can be used to yield asymptotically unbiased estimates. Experimental results for simulated linearized lateral aircraft motion in a digital closed loop mode are presented, showing the utility of the identification schemes.
Kotob, S.↗
Magnetic field directional discontinuities. I - Minimum variance errors
The paper deals with a statistical analysis of the errors associated with a minimum variance analysis of directional discontinuities by use of an idealized model of these discontinuities and various simulations, and also by an examination of actual Mariner 10 interplanetary magnetic field data. An empirical expression is derived for the magnitude of the error in an estimated discontinuity normal component, relative to the total field across the directional discontinuity. The analysis was performed primarily to aid in differentiating between interplanetary tangential and rotational discontinuities observed by Mariner 10.
Lepping, R. P.↗
A Minimum Variance Algorithm for Overdetermined TOA Equations with an Altitude Constraint.
We present a direct (non-iterative) method for solving for the location of a radio frequency (RF) emitter, or an RF navigation receiver, using four or more time of arrival (TOA) measurements and an assumed altitude above an ellipsoidal earth. Both the emitter tracking problem and the navigation application are governed by the same equations, but with slightly different interpreta- tions of several variables. We treat the assumed altitude as a soft constraint, with a specified noise level, just as the TOA measurements are handled, with their respective noise levels. With 4 or more TOA measurements and the assumed altitude, the problem is overdetermined and is solved in the weighted least squares sense for the 4 unknowns, the 3-dimensional position and time. We call the new technique the TAQMV (TOA Altitude Quartic Minimum Variance) algorithm, and it achieves the minimum possible error variance for given levels of TOA and altitude estimate noise. The method algebraically produces four solutions, the least-squares solution, and potentially three other low residual solutions, if they exist. In the lightly overdermined cases where multiple local minima in the residual error surface are more likely to occur, this algebraic approach can produce all of the minima even when an iterative approach fails to converge. Algorithm performance in terms of solution error variance and divergence rate for bas eline (iterative) and proposed approach are given in tables.
97 MATHEMATICS AND COMPUTING↗
Unscented Kalman Filter-based Unbiased Minimum-Variance Estimation for Nonlinear Systems with Unknown Inputs
This paper proposes an unscented Kalman filter (UKF)-based unbiased minimum-variance estimation (UMV) method for the nonlinear system with unknown inputs. By utilizing the statistical linerization, the nonlinear system and measurement functions are transformed into a “linear-like” regression form. The latter preserves the nonlinearity of the system and the measurement models. To this end, the unknown inputs can be estimated by the weighted least-squares. This “linear-like” regression form also allows us to resort to the UMV state estimation framework for the development of new nonlinear filter to handle unknown inputs. Specifically, two approaches have been developed: 1) given the estimated inputs, we derive a filter by minimizing the trace of the state error covariance matrix; 2) without input estimation, we derive the filter by minimizing the trace of the state error covariance matrix subject to a constraint imposed on the gain matrix. We prove that these two approaches provide the same results. Numerical results validate the effectiveness of the proposed method.
Unscented Kalman filter, unbiased minimumvariance ↗
Minimum variance geographic sampling
Resource inventories require samples with geographical scatter, sometimes not as widely spaced as would be hoped. A simple model of correlation over distances is used to create a minimum variance unbiased estimate population means. The fitting procedure is illustrated from data used to estimate Missouri corn acreage.
Terrell, G. R.↗
Linear minimum variance filters applied to carrier tracking
A new approach is taken to the problem of tracking a fixed amplitude signal with a Brownian-motion phase process. Classically, a first-order phase-lock loop (PLL) is used; here, the problem is treated via estimation of the quadrature signal components. In this space, the state dynamics are linear with white multiplicative noise. Therefore, linear minimum-variance filters, which have a particularly simple mechanization, are suggested. The resulting error dynamics are linear at any signal/noise ratio, unlike the classical PLL. During synchronization, and above threshold, this filter with constant gains degrades by 3 per cent in output rms phase error with respect to the classical loop. However, up to 80 per cent of the maximum possible noise improvement is obtained below threshold, where the classical loop is nonoptimum, as demonstrated by a Monte Carlo analysis. Filter mechanizations are presented for both carrier and baseband operation.
Gustafson, D. E.↗
Formulation of a minimum variance deconvolution technique for compensation of pneumatic distortion in pressure sensing devices
Increasingly, aircraft system designs require that aerodynamic parameters derived from pneumatic measurements be employed as control-system feedbacks. Such high frequency pressure measurements' accuracy is compromised by pressure distortion due to frictional attenuation and pneumatic resonance within the sensing system. A pneumatic distortion model is here formulated and reduced to a low-order state-variable model which retains most of the full model's dynamic characteristics. This reduced-order model is coupled with standard results from minimum variance estimation theory to develop an algorithm to compensate for pneumatic-distortion effects.
Whitmore, Stephen A.↗
Minimum Variance Orbit Determination Program First Quarterly Status Report 22 Jul. - 22 Oct. 1963
In this report are presented detailed technical summaries of the effort accomplished on Phase II of the Orbit Determination Program for the period July 22 to October 22, 1963, inclusive. Efforts carried out under Phase I which are applicable to the precision version of the Program are reported here as well.
MINIMUM VARIANCE ORBIT DETERMINATION↗
Minimum-variance fixed-form compensation of linear systems
The problem of determining the linear time-invariant compensator of a specified dimension which minimizes the asymptotic expected value of a quadratic form in the state variables of a linear stochastic system of arbitrary order, is considered. It is shown that under appropriate assumptions, the solution of this problem can be interpreted as a minimum-order observer-based or dual observer-based compensator for an optimally aggregated model of the plant.
Johnson, T. L.↗
Goddard minimum variance orbit determination progam
Real time tracking & orbit prediction program for circular, elliptical, & hyperbolic orbits with perturbation forces
REAL TIME↗
Minimum variance estimates of signal derivatives - A problem in instrument landing systems
Kalman filter application as observer of observable signals derivatives, using gain matrix to minimize variance estimate for instrument landing systems
Martin, J. C.↗
Evaluation of minimum variance estimators for signal derivatives in real noise environments
A digital filter has been developed to optimally estimate the rate of descent of an aircraft to permit all-weather operation. The filter was evaluated using recorded rate of descent information from flight instruments. The evaluation showed the filter to be superior to other systems especially when operated in turbulent air.
Snelsire, R. W.↗
A fully redundant double difference algorithm for obtaining minimum variance estimates from GPS observations
In double differencing a regression system obtained from concurrent Global Positioning System (GPS) observation sequences, one either undersamples the system to avoid introducing colored measurement statistics, or one fully samples the system incurring the resulting non-diagonal covariance matrix for the differenced measurement errors. A suboptimal estimation result will be obtained in the undersampling case and will also be obtained in the fully sampled case unless the color noise statistics are taken into account. The latter approach requires a least squares weighting matrix derived from inversion of a non-diagonal covariance matrix for the differenced measurement errors instead of inversion of the customary diagonal one associated with white noise processes. Presented is the so-called fully redundant double differencing algorithm for generating a weighted double differenced regression system that yields equivalent estimation results, but features for certain cases a diagonal weighting matrix even though the differenced measurement error statistics are highly colored.
Melbourne, William G.↗
What determines the direction of minimum variance of the magnetic field fluctuations in the solar wind?
The solar wind is not an isotropic medium; two symmetry axis are provided, first the radial direction (because the mean wind is radial) and second the spiral direction of the mean magnetic field, which depends on heliocentric distance. Observations show very different anisotropy directions, depending on the frequency waveband; while the large-scale velocity fluctuations are essentially radial, the smaller scale magnetic field fluctuations are mostly perpendicular to the mean field direction, which is not the expected linear (WkB) result. We attempt to explain how these properties are related, with the help of numerical simulations.
Grappin, R.↗
Minimum-Variance Control Allocation Considering Parametric Model Uncertainty
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Jared A. Grauer↗
On the angle between the average interplanetary magnetic field and the propagation direction of plane large amplitude Alfven waves
The paper shows that the experimentally observed close alignment of magnetic field minimum variance direction with the average magnetic field for Alfven waves in the solar wind is consistent with theoretically predicted properties of plane large amplitude Alfven waves in the MHD approximation. The theoretical properties of these Alfven waves constrain the time averaged magnetic field to cluster around the direction of minimum variance, which is aligned with the wave normal. Thus, spacecraft magnetometer observations in the solar wind of minimum variance directions strongly peaked about the average magnetic field direction are consistent with plane large amplitude Alfven waves which have wave normals aligned with the directions of minimum variance. This does not imply that geometrical hydromagnetic calculations for Alfven wave propagation direction in the solar wind are incorrect, but there is a discrepancy between geometrical hydromagnetics theory and observations that IMF minimum variance directions tend to be aligned with the ideal Parker spiral instead of the radial direction.
Lichtenstein, B. R.↗