Boonton 4530 Peak Power Meter User Manual - Manual (Page 163)

Boonton Electronics Chapter 5
4530 Series RF Power Meter Making Measurements
5-21
Step 6: The Sensor Temperature Drift Error depends on how far the temperature has drifted from the sensor calibration
temperature, and the temperature coefficient of the sensor. In our case, we are using a temperature compensated
sensor, and the temperature has drifted by 11 degrees C (49C - 38C) from the AutoCal temperature. We will use the
equation in the previous section to calculate sensor temperature drift uncertainty.
U
SnsrTempDrift
= ± (0.93% + 0.069% /degreeC)
= ± (0.93 + (0.069
0 11.0)) %
= ± 1.69%
Step 7: This is a relatively high-level measurement, so the noise contribution of the sensor is probably negligible, but
we’ll calculate it anyway. We’ll assume modulate mode with default filtering. The signal level is 13dBm, or 20mW. The
“noise and drift” specification for the 57518 sensor is 50nW, from the sensor’s datasheet. Noise uncertainty is the ratio
of these two figures.
U
Noise&Drift
= ± Sensor Noise (in watts) / Signal Power (in watts)
= ±50.0e-9 / 20.0e-3
0 100 %
= ±0.0003%
Step 8: A separate Sensor Zero Drift calculation does not need to be performed for peak sensors, since “noise and
drift” are combined into one specification, so we’ll just skip this step.
Step 9: The Sensor Calfactor Uncertainty needs to be interpolated from the uncertainty values in the Boonton
Electronics Power Sensor Manual. At 1 GHz, the sensor’s calfactor uncertainty is 2.23%, and at 0.5GHz it is 1.99%.
Note, however, that we are performing our AutoCal at a frequency of 1GHz, which is very close to the measurement
frequency. This means that the calfactor uncertainty cancels to zero at 1GHz, as discussed in the previous section.
We’ll use linear interpolation between 0.5GHz and 1GHz to estimate a value. 900MHz is only 20% (one fifth) of the way
from 1GHz down to 500MHz, so the uncertainty figure at 0.5GHz can be scaled by one fifth.
U
CalFactor
= 1.99
0 (900
- 1000) / (500 - 1000)
= 1.99
0 0.2
= ±0.40%
Step 10: Now that each of the individual uncertainty terms has been determined, we can combine them to calculate the
worst-case and RSS uncertainty values:
U (±%) K (U
0
K) 2 ( % 2 )
1. instrument uncertainty 0.20 0.500 0.0025
2. calibrator level uncertainty 3.11 0.577 3.2201
3. calibrator mismatch uncertainty 1.27 0.707 0.8062
4. source mismatch uncertainty 0.80 0.707 0.3199
5. sensor shaping error uncertainty 2.00 0.577 1.3333
6. sensor temperature drift uncertainty 1.69 0.577 0.9509
7. sensor noise & drift uncertainty 0.00 0.500 0.0000
8. sensor calibration factor uncertainty 0.40 0.500 0.0400
___________________________
Total worst case uncertainty: ±18.43%
Total sum of squares: 6.6729 % 2
Combined Standard uncertainty U
C
(RSS) : ±2.58 %
Expanded Uncertainty U (coverage factor k = 2) : ±5.17 %
From this example, different error terms dominate. Since the measurement is close to the calibration frequency, and
matching is rather good, the shaping and level errors are the largest. Expanded uncertainty of 5.16% translates to an
uncertainty of about 0.22dB in the reading.