Last week I published my results about the LilacSat-2 VLBI experiment. There, I mentioned that there were some things I still wanted to do, such as studying the biases in the calculations or trying to improve the signal processing. Since then, I have continued working on this and I have tried out some ideas I had. These have given good results. For instance, I have been able to reduce the delta-range measurement noise from around 700m to 300m. Here I present the improvements I have made. Reading the previous post before this one is highly recommended. The calculations of this post were performed in this Jupyter notebook.
On 23 February, Wei Mingchuan BG2BHC published on Twitter the first Amateur VLBI experiment. This consisted of a GPS-synchronized recording of signals from LilacSat-2 using USRPs in groundstations at Harbin and Chongqing, which are about 2500km apart. Wei has made a Github repository containing the recording (in MATLAB file format) and some signal processing in MATLAB. I have done some signal processing of my own with the recording and published my results in a Jupyter notebook. Here I describe some general aspects about VLBI and its use in Amateur radio, and some specific details of the signal processing I have done.
On January 28th, Tetsu JA0CAW reported on Twitter his reception of an unknown satellite. The time of reception was 2018-01-28 12:15 UTC and the frequency was around 435.525MHz. The time and frequency coincided with a PicSat pass over JA0CAW’s station in Japan. He provided an IQ recording of the signal. So far, the satellite that originated the signal has not been identified. Several people have tried to listen to this satellite again, but I haven’t seen any other reports. Doppler identification has not been attempted and it is perhaps unfeasible with the few packets in JA0CAW’s recording.
I have looked at the recording to try to identify the satellite. The modulation is easily seen to be BPSK at 9600baud. The signal presents a lot of fading, so demodulation without bit errors is difficult. There seems to be a scrambler in use. I’ve tried descrambling with G3RUH and CCSDS without any luck. I’ve also failed to identify a preamble or frame sync marker.
To look at the packets in more detail, I’ve resorted to do demodulation as postprocessing in a Jupyter Python notebook. The resulting notebook is here. It is written with detailed comments, so it can be of interest to anyone who wants to learn these techniques.
The only interesting piece of information that I’ve been able to extract from my analysis is that the bits in the packets present strong self-correlations at lags of 1920 bits (and multiples). This is 240 bytes, but I have no clue of what to make of this.
As always, I would be grateful if anyone can provide any additional information about this unknown satellite.
CODAR is an HF radar used to measure surface ocean currents in coastal areas. Usually, it consists of a chirp which repeats every second. The chirp rate is usually on the order of 10kHz/s, and the signal is gated in small pulses so that the CODAR receiver can listen between pulses. The gating frequency can be on the order of 1kHz.
CODAR can be received by skywave many kilometers inland. Being a chirped signal, it is easy to extract the multipath information from the received signal. In this way, one can see the signal bouncing off the different layers of the ionosphere, and magnificent pictures showing the changes in the ionosphere (especially at dawn and dusk) can be obtained. For instance, see these images by Pieter Ibelings N4IP, or the image at the top of this post, which contains 48 hours worth of CODAR data.
A few days ago I read the paper about the Breakthrough Listen experiment. This experiment consists in doing many wideband recordings of different stars using the Green Bank Telescope, and (in the future) Parkes Observatory and then trying to find signals from extraterrestrial intelligent life in the recordings. The Breakthrough Listen project has a nice Github repository with some documentation and an analysis of a recording they did of Voyager 1 to test their setup.
I have also been thinking about how to study the polarization of signals in a dual polarization recording (two coherent channels with orthogonal polarizations). My main goal for this is to study the polarization of the signals of Amateur satellites in low Earth orbit. It seems that there are many myths regarding polarization and the rotation of cubesats, and these myths eventually pop up whenever anyone tries to discuss whether linearly polarized or circularly polarized Yagis are any good for receiving cubesats.
Through the Breakthrough Listen paper I’ve learned of the Stokes parameters. These are a set of parameters to describe polarization which are very popular in optics, since they are easy to measure physically. I have immediately noticed that they are also easy to compute from a dual polarization recording. In comparison with Jones vectors, Stokes parameters disregard all the information about phase, but instead they are computed from the averaged power in different polarizations. This makes their computation less affected by noise and other factors.
As I also wanted to get my hands on the Breakthrough Listen raw recordings, I have been computing the Stokes parameters of the Voyager 1 signal in their recording. Since the Voyager 1 signal is left hand circularly polarized, the results are not particularly interesting. It would be better to use a signal with changing polarization or some form of elliptical polarization.
I have started to use Jupyter notebook. This is something I had been wanting to try since a while ago, and I’ve realised that a Jupyter notebook serves better to document my experiments in Python than a Python script in a gist, which is what I was doing before. I have started a Github repo for my experiments using Jupyter notebooks. The experiment about polarization in the Voyager 1 signal is the first of them. Incidentally, this experiment has been done near Voyager 1’s 40th anniversary.
In a previous post I discussed my BER simulations with the LilacSat-1 receiver in gr-satellites. I found out that the “Feed Forward AGC” block was not performing well and causing a considerable loss in performance. David Rowe remarked that an AGC should not be necessary in a PSK modem, since PSK is not sensitive to amplitude. While this is true, several of the GNU Radio blocks that I’m using in my BPSK receiver are indeed sensitive to amplitude, so an AGC must be used with them. Here I look at these blocks and I explain the new AGC that I’m now using in gr-satellites.
Lately, I have been playing around with the concept of doing acquisition and wipeoff of JT9A signals, using a locally generated replica when the transmitted message is known. These are concepts and terminologies that come from GNSS signal processing, but they can applied to many other cases.
In GNSS, most of the systems transmit a known spreading sequence using BPSK. When the signal arrives to the receiver, the frequency offset (given by Doppler and clock error) and delay are unknown. The receiver runs a search correlating against a locally generated replica signal which uses the same spreading sequence. The correlation will peak for the correct values of frequency offset and delay. The receiver then mixes the incoming signal with the replica to remove the DSSS modulation, so that only the data bits that carry the navigation message remain. This process can be understood as a matched filter that removes a lot of noise bandwidth. The procedure is called code wipeoff.
The same ideas can be applied to almost any kind of signal. A JT9A signal is a 9-FSK signal, so when trying to do an FFT to visually detect the signal in a spectrum display, the energy of the signal spreads over several bins and we lose SNR. We can generate a replica JT9A signal carrying the same message and at the same temporal delay than the signal we want to detect. Then we mix the signal with the complex conjugate of the replica. The result is a CW tone at the difference of frequencies of both signals, which we call wiped signal. This is much easier to detect in an FFT, because all the energy is concentrated in a single bin. Here I look at the procedure in detail and show an application with real world signals. Recordings and a Python script are included.
Several weeks ago, in an AMSAT EA informal meeting, Eduardo EA3GHS wondered about the possibility of using WSJT-X modes through linear transponder satellites in low Earth orbit. Of course, computer Doppler correction is a must, but even under the best circumstances we cannot assume a perfect Doppler correction. First, there are errors in the Doppler computation because the TLEs used are always measured at an earlier time and do not reflect exactly the current state of the satellite. This was the aspect that Eduardo was studying. Second, there are also errors because the computer clock is not perfect. Even a 10ms error in the computer clock can produce a noticeable error in the Doppler computation. Also, usually there is a delay between the time that the RF signal reaches the antenna and the time that the Doppler correction is computed for and applied to the signal, especially if using SDR hardware, which can have large buffers for the signal. This delay can be measured and compensated in the Doppler calculation, but this is usually not done.
Here we look at errors of the second kind. We denote by \(D(t)\) the function describing the Doppler frequency, where \(t\) is the time when the signal arrives at the antenna. We assume that the correction is not done using \(D(t)\), but rather \(D(t – \delta)\), where \(\delta\) is a small constant. Thus, a residual Doppler \(D(t)-D(t-\delta)\) is still present in the received signal. We will study this residual Doppler and how tolerant to it are several WSJT-X modes, depending on the value of \(\delta\).
The dependence of Doppler on the age of the TLEs will be studied in a later post, but it is worthy to note that the largest error made by using old TLEs is in the along-track position of the satellite, and that this effect is well modelled by offsetting the Doppler curve in time. This justifies the study of the residual Doppler \(D(t)-D(t-\delta)\).
Last weekend, I recorded the full EAPSK63 contest in the 40m band with the goal of monitoring IMD levels. I made a 48kHz IQ recording spanning the full 24 contest hours (from 16:00 UTC on Saturday to 16:00 UTC on Sunday). This week I’ve been playing with making waterfall plots from the recording. These are very interesting, showing patterns in propagation and contest activity. Here I show some of the waterfalls I’ve obtained, together with the Python code used to compute them.
This weekend I have recorded the full EAPSK63 Spanish PSK63 contest in the 40m band with the goal of playing back the recording later and reporting the stations showing excessively high IMD levels. In PSK contests, it is usual to see terribly distorted signals, which are the result of reckless operating techniques and stations which are setup inadequately. Contest rules don’t help much, as they are usually too weak to prevent distorted signals from interfering other participants. Amateurs should take care and strive to produce a signal as clean as possible. For instance, in the US, Part 97 101 a) states that “each amateur station must be operated in accordance with good engineering and good amateur practice”. Here I describe the signal processing done in this study and list a “hall of shame” of the worst stations I have spotted in my recording. I will notify by email the contest manager and all the stations in this list with the hope that the situation improves in the future.