HP 8719ES, Справочное руководство

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10-2
Determining System Measurement Uncertainties
Introduction
Introduction
In any measurement, certain measurement errors associated with the system add
uncertainty to the measured results. This uncertainty defines how accurately a device
under test (DUT) can be measured. This chapter describes how the various network
analyzer measurement error sources contribute to uncertainties in the magnitude and
phase measurements of both transmission and reflection.
Network analysis measurement errors can be separated into two types: raw and residual.
The raw error terms are the errors associated with the uncorrected system. Network
analyzer errors can be classified as systematic (repeatable), random (non-repeatable), and
drift. The residual error terms are the errors that remain after a measurement calibration.
The error correction procedure, also called measurement calibration, measures a set of
calibration devices with known characteristics. It uses the measurement results to
effectively remove systematic errors, using the vector math capabilities of the analyzer.
Differences between calibration standard measured and modeled responses yield residual
errors. The residual systematic errors remain after error correction, primarily due to the
limitations of how accurately the electrical characteristics of the calibration devices can be
defined and determined. Random errors cannot be corrected because their contribution is
not constant between calibration and measurement. However, the effects of random errors
can be reduced through averaging. Drift errors are caused by ambient temperature
variation and component aging. The residual systematic errors along with the random and
drift errors continue to affect measurements after error correction, adding an uncertainty
to the measurement results. Therefore, measurement uncertainty is defined as the
combination of the residual systematic (repeatable), random (non-repeatable), and drift
errors in the measurement system after error correction.
The following measurement uncertainty equations show the relationship of the systematic,
random, and drift errors. These are useful for predicting overall measurement
performance.