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Pointing Error Analysis Tool - Computational Techniques for Evaluating the Pointing Error Metrics PDF Print E-mail
Article Index
Pointing Error Analysis Tool
Pointing Error Definitions
Concepts from Optics
Standard Pointing Error Metrics
Computational Techniques for Evaluating the Pointing Error Metrics
Why Not Use the Peak Stability or Other Metric?
Pointing Error Analysis
Further Reading
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Computational Techniques for Evaluating the Pointing Error Metrics

The pointing error metrics are evaluated quickly and accurately from time-domain data by using fast computational algorithms that utilize the Discrete Fourier Transform to convert the time-domain data to the frequency domain. The frequency domain equations for the pointing error metrics are then evaluated by summing a weighted mean square response. A different weighting is used for each metric. Individual contributions to the pointing error metrics can be determined to determine the most offending disturbance sources and to determine sensitivities to disturbances.

Frequency-domain data can also be obtained from the frequency response of a transfer function. It is particularly fast and easy to evaluate the pointing error metrics for reaction wheel or CMG disturbances.

For stochastic (random) disturbances, one of two paths can be taken to evaluate the pointing error metrics. One path is to simulate random inputs in a time-domain simulation, and then evaluate the metrics using the time-domain response. The other path is to augment the frequency-domain plant model with weighting functions. These weighting functions are linear transfer functions that approximate the weighting functions in the pointing error metrics. The covariance (or variance) at the output of the transfer functions is then evaluated by using standard methods available in Matlab. This path is often the fastest.