Search

Perceptions of supervisor bias can profoundly affect organizational climate and employees' morale. Based on attribution theory, it was predicted that bias would be used to explain denial of promotion when the employee had been expected to succeed. It was also predicted that social comparison information detailing the similar plight of peer women or the good fortune of peer men would enhance attributions of sexism. College students (N=60) read the career histories of women who had been denied promotion by their male bosses. Expectations of success were manipulated by presenting information that indicated level of ability and quality of the employee's past performance. Participants explained the denial of promotion by rating the employee's ability and effort, the difficulty of her job, and the supervisor's sexism and incompetence. Results supported predictions. Magnitude of attributed sexism was greater when the employee was described as having both high ability and an excellent record of past performance. Both information concerning the similar plight of peer women and the good fortune of peer men increased the magnitude of attributed sexism. Further support for an attributional model of bias was obtained from ratings of employees' effort and job difficulty. Limitations of the generality of the findings are discussed, and references are include, along with three data analysis graphs. (Author/TE)

Possibilities are explored for applying estimation theory to the analysis, interpretation, and use of air quality measurements in conjunction with simulation models to provide a cost effective method of obtaining reliable air quality estimates for wide urban areas. The physical phenomenology of real atmospheric plumes from elevated localized sources is discussed. A fluctuating plume dispersion model is derived. Individual plume parameter formulations are developed along with associated a priori information. Individual measurement models are developed.

The J-Adaptive estimator described by Jazwinski and Hipkins (1972) is extended to include the simultaneous estimation of the statistics of the unmodeled system accelerations. With the aid of simulations it is demonstrated that the J-Adaptive estimator with estimated noise statistics can automatically estimate satellite orbits to an accuracy comparable with the data noise levels, when excellent, continuous tracking coverage is available. Such tracking coverage will be available from satellite-to-satellite tracking.

A sequential estimator is presented and demonstrated which successfully tracks the system state and model errors in the presence of significant and unpredictable system or environmental variations. This adaptive estimation concept is shown to lead to a new and attractive approach to parameter identification problems. Simulation results are presented in an orbit determination problem, where the estimator tracks the orbit and unmodeled accelerations due to errors in the geopotential model. Some results in a re-entry trajectory estimation problem are also summarized.

Estimation theory, which originated in guidance and control research, is applied to the analysis of air quality measurements and atmospheric dispersion models to provide reliable area-wide air quality estimates. A method for low dimensional modeling (in terms of the estimation state vector) of the instantaneous and time-average pollutant distributions is discussed. In particular, the fluctuating plume model of Gifford (1959) is extended to provide an expression for the instantaneous concentration due to an elevated point source. Individual models are also developed for all parameters in the instantaneous and the time-average plume equations, including the stochastic properties of the instantaneous fluctuating plume.

Estimation theory has been applied to a wide range of aerospace problems. Application of this expertise outside the aerospace field has been extremely limited, however. This paper describes the use of covariance error analysis techniques in evaluating the accuracy of pollution estimates obtained from a variety of concentration measuring devices. It is shown how existing software developed for aerospace applications can be applied to the estimation of pollution through the processing of measurement types involving a range of spatial and temporal responses. The modeling of pollutant concentration by meandering Gaussian plumes is described in some detail. Time averaged measurements are associated with a model of the average plume, using some of the same state parameters and thus avoiding the problem of state memory. The covariance analysis has been implemented using existing batch estimation software. This usually involves problems in handling dynamic noise; however, the white dynamic noise has been replaced by a band-limited process which can be easily accommodated by the software.

The development and testing of nonlinear and adaptive estimators for reentry (e.g. space shuttle) navigation and model parameter estimation or identification are reported. Of particular interest is the identifcation of vehicle lift and drag characteristics in real time. Several nonlinear filters were developed and simulated. Adaptive filters for the real time identification of vehicle lift and drag characteristics, and unmodelable acceleration, were also developed and tested by simulation. The simulations feature an uncertain system environment with rather arbitrary model errors, thus providing a definitive test of estimator performance. It was found that nonlinear effects are indeed significant in reentry trajectory estimation and a nonlinear filter is demonstrated which successfully tracks through nonlinearities without degrading the information content of the data. Under the same conditions the usual extended Kalman filter diverges and is useless. The J-adaptive filter is shown to successfully track errors in the modeled vehicle lift and drag characteristics. The same filter concept is also shown to track successfully through rather arbitrary model errors, including lift and drag errors, vehicle mass errors, atmospheric density errors, and wind gust errors.

Computer program for open-loop error analysis of low thrust interplanetary trajectories

Book on stochastic processes and filtering theory covering probability theory, Markov processes, linear and nonlinear filters, etc

Adaptive filtering to prevent divergence observed in application of Kalman filter to orbit determination

Adaptive filter for estimating system noise inputs from actual residuals developed to control Kalman filter in satellite orbit estimation

Filtering and optimal control problems for discrete stochastic dynamical systems, describing various feedback controls and midcourse guidance optimization

Limited memory optimal filter theory, output and standard filter divergence due to errors

Optimal stochastic midcourse guidance, analyzing correction programs employing feedback gain matrix

Compensation for dynamic and measurement model errors in real time orbit determination systems

Computer program plan for simulating real time observation schedules and combined effects of dynamic model errors in three-dimensional satellite motion

Adaptive filter concepts for preventing divergence in applying Kalman filter to orbit calculation

Linear and nonlinear limited memory optimal filters for computing conditional probability density function

Adaptive filtering to prevent divergence observed in application of Kalman filter to orbit determination

Adaptive filter with inaccuracies approximated by Gaussian white noise input and determination of covariance for most probable residual sequence

Feedback from observations introduced into midcourse guidance correction program, showing optimal random feedback solution obtained for deterministic optimal feedback control problems

Filtering for nonlinear dynamical systems with white Gaussian noise processes

The J-adaptive sequential estimator is extended to include simultaneous estimation of the noise statistics in a model for system dynamics. This extension completely automates the estimator, eliminating the requirement of an analyst in the loop. Simulations in satellite orbit determination demonstrate the efficacy of the sequential estimation algorithm.

A sequential estimator is presented and demonstrated which successfully tracks the system state and model errors in the presence of significant and unpredictable system or environmental variations. This adaptive estimation concept is shown to lead to a new and attractive approach to parameter identification problems. Simulation results are presented in an orbit determination problem and in a reentry trajectory estimation problem.

Techniques are studied for the estimation of rigid body and bending states and the identification of model parameters associated with the single-axis attitude dynamics of a flexible vehicle. This problem is highly nonlinear but completely observable provided sufficient attitude and attitude rate data is available and provided all system bending modes are excited in the observation interval. A sequential estimator tracks the system states in the presence of model parameter errors. A batch estimator identifies all model parameters with high accuracy.

The problem of real-time estimation of a lifting reentry vehicle trajectory of the shuttle orbiter type is considered. Simulations feature large position and velocity uncertainties at radar acquisition and realistic model errors in lift, drag and other model parameters. Radar tracking and accelerometer data are simulated. Significant nonlinearities are found to exist on spacecraft acquisition. An iterated nonlinear filter is shown to perform optimally during the radar acquisition phase. An adaptive filter is shown to track time-varying model errors, such as errors in the lift and drag coefficients, down to the noise level. Such real-time model tracking (identification) is frequently required for guidance and control implementation.