2018-11-28 01:40:51 UTC
I’ve been monitoring the Doppler shift of WWV/H and CHU for a while now from my location 100 km / 60 mi NW of Seattle.
The receiver is a QS1R, using an LTC2208 16 bit ADC, and the 125 MHz clock is provided by a Bodnar GPSDO. A Wellbrook ALA1530S+ loop antenna provides good coverage for the entire range.
I use a Python script to tune the QS1R to each frequency for 11 seconds while in CW mode. The 800 Hz beat frequency is measured using the open source program Fldigi.
Fldigi is mainly intended for HF digital modes like RTTY and PSK31, but the “Frequency Analysis” function is effectively an audio frequency counter with .001 Hz resolution, updated every 1.024 seconds. The first 5 or 6 samples are discarded after a frequency change. The signal must be within about ± 5 Hz.
During many minutes, WWV transmits a 500 Hz tone, and WWVH a 600 Hz tone (or the reverse). By treating these as separate carriers (e.g. tuning to 5.0006 MHz) I'm able to separate the Doppler shift and signal strength of the two stations. It's remarkable that Fldigi is able to make an accurate measurement of the 500/600 Hz "carriers", since there is a 40 ms gap surrounding the tick sound each second.
I've just recently got enough of a handle on Python, Matplotlib, Pandas, JupyterLab, and other excellent tools to even begin to analyze and display the data.
From my location:
WWV: Ft Collins, CO 1,675 km, 1,040 mi, 113°
WWVH: Kauai, HI 4,335 km, 2,694 mi, 241°
Each datapoint for the 500/600 Hz signal is plotted, to give a sense of the signal variation. Only the smoothed carrier signal is plotted, because otherwise it made the graph "too" busy (ha). The 500/600 Hz plots are moved up by 15 dB to correspond with the carrier level. But this means their displayed noise floor is raised as well. For instance, the WWVH signal from 18:00 until past 00:00 is mostly in the noise.
Each observation is for 5 or 6 seconds, and taken a few minutes apart. If the standard deviation of the observation exceeds 0.150 Hz it is rejected. The reject is plotted in light gray, but otherwise ignored. That's the only filter being applied to the data. WWV is usually stronger at my location, and the carrier data correlates with it pretty closely. Based on looking at a few days data, usually WWV gives a more accurate and higher confidence reading than the carrier alone, and considerably better than WWVH. Last week the largest daily 5 MHz WWV median was .010 Hz (2.0e-09); one other day was .003, but most were .001 (2.0e-10).
I'm just now getting presentable results, and expect to find errors: cosmetic, conceptual, and fundamental. Currently the Python script is as shaky as my understanding of basic statistics.
Oh well, back to the data
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