Category Archives: Software

CIS Time Signals on VLF

Locations, callsigns and starting times of the received VLF time signal stations, 25 kHz

On January 10, 2020, I did a round-up of VLF time stations from the Commonwealth of Independant States (CIS). They are controlled by the Russian Navy and start their main transmission on 25.0kHz. Then they change to a couple of four other VLF channels. See here for some detailed information in Russian. The diagram below shows a panorama of all received station (Khabarovsk in the Far East missing, as they skip transmission on the 10., 20. and 30. each month) on all frequencies versus time and signal level.

Five locations, six transmissions, five frequencies – this diagram puts it all together.

The diagram features a time resolution of 1s and has a resolution bandwidth of about 0.12 Hz. It is part of a 24h session, made with Winradio’s Excalibur Sigma SDR, active dipole MD300DX (2x5m) and Simon Brown’s software SDRC V3. This software delivers also the values for level over time, which were visualized and combined with QtiPlot software.

Only seemingly, Vileyka and Krasnodar are transmitting on two channels at the same time (from 07:06 UTC/11:06 UTC). This is not the case, but their transmitters show a bit wider signal in their first part of the transmission. Thus, the much weaker (ca. -30dB) “signal” at the same time, but 100Hz up, is some kind of sideband, but not the carrier!

You will see some variation of the carrier power, especially following sign on, but also during the transmission. This can bee seen with tenfold time resolution (i.e. 100ms) and magnifying the dB-scale, see diagram at the bottom as just one example. Fading can be largely excluded for several reasons, artificial characteristic of changes and VLF propagation during short periods among them.

Under the microscope: This rise of 1.5dB of the carrier is part of the workflow of switching on/tuning the transmitter. There are many such details, and they may differ from transmitter, location and performance. Such details might be used for “fingerprinting”.

P.S. The map at the top was made with free software Tableau Public. The locations are geo-referenced, and a satellite map as background will you lead directly to the antennas. Please try this here.

FAX from Shanghai: Pacific Pressures

This FAX broadcast was new to me and received on December 16, 2019 at 08:20 UTC on 16557,1 kHz. It was transmitted via Shanghai Coastal Radio, presumably directed into the Pacific, of which it shows the 48h surface pressure.

It was demodulated from a 25 MHz wide HF recording over 24 hours. This recording was made with Winradio’s G65DDCe Sigma SDR, connected to an active vertical MegaDipol MD300DX (2 x 5 m), and decoded with Wavecom’s W-Code. The recording was scheduled with software SDRC V3 by Simon Brown, and directed via USB3.1 to a 20TB hard disk, WD Duo Book. The resulting one file was 8TB, format WAV RF64.

It was also played back from this hard disk, also via USB3.1. Doing so, it is most remarkable that this setup worked smoothly without any glitches which would promptly have been seen at such a time-critical mode like this FAX., 120/576. So, this reception is also a proof that one can work smoothly with such ‘big data’ even on a hard disk – and not only on expensive SSDs. A FAX transmission is that sensitive that you even see a very weak echo (best seen of the big vertical black stripe at the right which echoes from around 115° East). This originates from a mixed short/long path reception. The strong short path’ flight time is 28.7ms, whereas the weak long path needed 104.7ms. As one FAX line covers 500ms, you can easily measure the delay of roughly 80ms, almost exactly matching the difference of long and short path.

The screenshot has been left un-retouched.

Looking at Things: Elad FDM-S3 [beta]

The new FDM-S3. Made in Italy, by ELAD.
19.7 MHz alias-free for receiving, recording and playing

As seen from now, ELAD’s FDM-S3 is still to come. It features a 16 bit SDR with up to 24 MHz bandwidth (19.7 MHz alias-free) for receiving, recording and playback. It will become the great brother of the renowned FDM-S2 of also 16 bit, but with just 5 MHz alias-free bandwidth which was State-of-the-Art when this radio hit the market. Still, this remarkable FDM-S2 sets the standard in its price class.

The file format of the S3 is the same as with the S2, so Simon Brown’s software SDRC V3 works on S3 files also (see screenshot at the bottom). This opens V3’s File Analyzer plus up to 24 demodulators when playbacking files. SDRC V3 will support also live reception when the radio will be more widely available.

Simon Brown’s software SDRC V3 is reading FDM-S3’s files also.
The FDM-S3 cover the whole FM band. Here shown with receiving six stations in parallel, including demodulation and RDS deocding. The lower half of the the spectrogram shows the performance without pre-amplifier, the upper part with pre-amplifier switched in.

Monitoring: Visualizing with free Tableau Public Software

Part of a multi-channel monitoring of the HFGCS net in ALE on July 14, 2019: the vertikal axis shows the channel, the horizontal axis the time of monitoring.

2019 is the year of groundbreaking Software-defined radios, covering the whole HF range of 30 MHz width and recording it for many hours, e.g. from midnight to midnight. In combination with proper software, this allows for a fresh view onto monitoring.

For the screenshot on the top, I had monitored nine HFGCS channels from 3137 kHz to 23327 kHz in parallel (the 18003 kHz didn’t work, sorry) with Winradio’s SIGMA SDR, running with Simon Brown’s free software SDRC V3 and nine instances of MultiPSK decoder.

After automatic monitoring, I harvested all time-stamped logs stripped them from information not needed, and imported them to free Tableau Public software to visualize activity according to station, time and channel. This gives an overview on the monitoring session, propagation, time sequences of hopping from channel to channel etc. – you might zoom into the screenshot for a clearer look.

Thanks to Tableaus also stunning geospatial features, completely other views of the same log are available. The screenshot below shows the number of logs on all channels of a monitoring session of 12 hours.

Geospatial information of the stations, combined with the number of log entries on all channels.

You may zoom into this OSM[ap], and you may also have a zoomed satellite view (or this or that) which directly hits the feeder point of your antenna … if you know the exact location and this is a part of your log entry – see screenshot below.

Zooming the map above onto JDG at satellite view, directly leads you to the location of the station – here Diega Garcia US Military base.

The most versatile Tableau software also allows to relaize many other ideas to visualize monitoring; some of them already above horizon, others still below. To conclude this entry, I did a visualization of all HF stations/channels of AFAD, the Turkish Disaster and Emergency Management Authority, heard by me over the last 18 months. Each (?) of the 81 Turkish provinces maintains an AFAD base, and all (most?) of them are communicating on HF. As Tableau has many detailed geographical already aboard, a visualization of channels/province being heard is easy.

Analyzing part of a logbook: All Turkish provinces heard with ALE signals of AFAD are colored – the deeper the color, the more channels were received in the last 18 months.

Dream Team: Winradio’s SIGMA and Simon’s Software (1)

All main six GMDSS channels on HF at once: Winradio’s SIGMA with Simon Brown’s software SDRC V3

Some days ago, I wrote about my very first experiences with Winradio’s groundbreaking SIGMA SDR receiver, covering e.g. the whole HF band with 32 MHz width and 16 bit resolution – plus much, much more. SIGMA comes with a fine software, and provides an API.dll for connection to 3rd-party software. Thankfully, Simon Brown, G4ELI, adapted his unique SDRC V3 software to this (and other) Winradio in nearly no time.

This combination has become a real dream team: the best hardware and the best software avalaible. The screenshot at the top shows just one example of others which will follow: I made a 24 hour recording of 0 to 25 MHz (7.85TB) and placed six demodulators on the main GMDSS channels on HF between 2 and 16 MHz. You see each channel in a separate window at the top of the screenshot, showing spectrum and spectrogram with time stamps of the recording. Below those six channels you see spectrum and spectrogram of the whole recorded bandwidth, namely 25 MHz. Eventually, below this spectrogram you see 60 x 24 boxes, one for every minute of the 24 hours recording. Just click into the time you want, and the recording instantaneoulsy to it.

Demodulated audio is guided via VAC1 … VAC6 to six different instances of the free YAND GMDSS decoder – see screenshot at the bottom.

There are great many other applications of this revolutionary combination to which I will come back later.

Parallel reception & decoding of six GMDSS channels at once.

ALE [MIL-STD-188-141A]: Which one is the best Decoder?

This is an update from my post two days ago. I have expanded the number of test signals and added some hints.

Does your decoder read this track? Buried in noise and plagued by multipath fading, the recordings below will separate the wheat from the whaff.

Often I am asked – and sometimes even asking myself! – “Which one is the best decoder for ALE?” This means: Which one delivers the best decoding under demanding conditions?

To test this, I made a recording of twelve stations “on the air” plus one weak signal, buried in Additive White Gaussian Noise, AWGN. All signals are correctly tuned, no one invers. All were read by at leastby one of my decoders “in a row”.

To test your decoders, you should download this WAV file of 131 seconds length and play it. It can be either directly opened by some decoder, or feed it via virtual audio cable (VAC) into a decoder. I used Audacity for this.

I am as interested in the results as you are – so please drop me a line to dk8ok [at] gmx.net. I like to encourage you to try all ALE decoders you have at hand – the more, the better.

Already the first results were surprising. This concerned both, the decoding ability of the decoders and the repeatability of the test. So far, the following decoders had participated: go2monitor, Krypto500, MARS-ALE, MultiPSK, Sorcerer, and W-Code. Steve, N2CKH, had written some valuable hints to optimize his MARS-ALE software for SIGINT purposes – please see his comment.


This WAV file contains the calls of thirteen ALE stations. Download and save this file (point to the icon, press right mouse button …). Then feed it to your decoders. Copy the results and send them to me. Have fun!

Visualizing HF Networks

G-VIFT

A flight of G-VFIT from Atlanta to Heathrow, and its HFDL communications – visualized by GEPHI.

Gephi does it

Each communications has a structure. Visualization reveals this structure. This is also valid for HF communications with its networks with different stations, (even moving) locations, hierarchy …

Recently, I made my first steps with free visualization software GEPHI to get a deeper look into some aeronautical networks. The graph at the top shows the gephi’ed result of 125+ HFDL messages, transmitted by a flight of G-VFIT from Atlanta to Heathrow. I monitored six HFDL channels in parallel, one channel from New York, two from Shannon, and three from Reykjavik.

Each point represents a message, tagged by its time in UTC. The positions of the points are geo-referenced, as I used HFDL messages containing these information.

Wheras the longitude’s positions are to scale, their latitude’s positions had been spread for better reading.

This visualization shows that Reykjavik on 6.712 kHz did the main work. But it is surprising that the first contact just leaving the U.S. coast was made with Shannon, and not with nearby New York.

GEPHI also helps in visualizing the hiearchy of networks, see screenshot below:


In the Russian Aero Net on 4.712 kHz, Rostov plays a pivotal role.

Here the strength of the connecting lines and the position of the city’s names represents to hierarchy of this network, i.e. who is calling whom, and how often. There are several strongholds like Rostov, Yekaterinburg and Samara, but also some mere outposts like Novosibirsk and Syktyvkar.

This picture isn’t geo-, but social-referenced, to say so: you know that e.g. Chelyabinsk on the left is geographically situated east of Rostov. You may also geo-reference these data, turn it into a kml file and see it in Google Maps of Open Street Map. If the co-ordinates are correct, zooming will take you exactly to the feeding point of each of their Nadenenko dipole, see below …

The above visualized hierarchy has been changed here into a geo-referenced kml file, opened in Google Earth.

There are many more applications of such a great tool for visualization which will further enrich monitoring.

« Older Entries