Polarization in Voyager signal from Green Bank Telescope

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.

AGC for gr-satellites

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.

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Acquisition and wipeoff for JT9A

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.

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WSJT-X and linear satellites: part I

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).

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Waterfalls from the EAPSK63 contest

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.

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Monitoring IMD levels in the EAPSK63 contest

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.

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Improving signal processing in my OTH radar receiver

This is a follow up post to my experiments studying OTH radar. I have found that the signal processing I did there to obtain the cross-correlation was far from optimal. This produced the strange side-bands below the main reflection. The keen reader might have noticed that I was doing the cross-correlation with a template pulse that lasted the whole pulse repetition cycle. However, the pulses from the radar are shorter. Therefore, it is a better idea to use a shorter template pulse. Ideally, the template pulse should have the same length as the transmitted pulse. However, I don't know this length precisely, because multipath propagation makes the received pulses a bit longer. However, I think that 6.5ms is a good estimate.

I have also changed the window for the pulse. I'm now using a Dolph-Chebyshev window. I use scipy to compute this window, because it is not included in GNU Radio. This window has the property that the side-bands have constant attenuation. Indeed, it minimizes the L^\infty norm of the side-bands. There is a parameter that tunes the side-bands attenuation. For higher attenuations, you have a wider main lobe, while if you want a narrower main love you get less side-band attenuation. These properties make this window useful in radar applications.

Below I'm doing the cross-correlation in GNU Radio with a shorter template pulse shaped with a Dolph-Chebyshev window set for 55dB attenuation.

Cross-correlation with shorter pulse

The good thing about the settable attenuation of the Dolph-Chebyshev window is that it can be used to trade-off performance between different features. First, we use an attenuation of 100dB. The side-bands are below the noise floor in this case, so we have no "false responses" in our cross-correlation. The drawback is that the main lobe is wide so the resolution of the features of the ionosphere in the image below is not very good.

Dolph-Chebyshev window with 100dB attenuation

Next we try with 55dB attenuation. This narrows the main lobe, improving the resolution and crispness of the features of the ionosphere in the image below. However, side-bands start being visible above the noise floor, producing "false responses". However, comparing with the image above, we now know where the false responses are.

Dolph-Chebyshev window with 55dB attenuation

I have updated the gist with the GNU Radio flowgraph and python script used to produce the images.

Scramblers and their implementation in GNUradio

A scrambler is a function that is applied to a sequence of data before transmitting with the goal of making this data more "random-like". For instance, scrambling avoids long runs of the bits 0 or 1 only, which may make the receiver loose synchronization or cause spectral concentration of the signal. The receiver applies the inverse function, which is called descrambler, to recover the original data. The documentation for the scrambler blocks in GNUradio is not very good and one may need to take a look at the implementation of these blocks to get their parameters right. Here, I'll try to explain a bit of the mathematics behind scramblers and the peculiarities of their implementation in GNUradio.

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Another look at recursive quadrature oscillators

In a recent post, we looked at which 2\times 2 Toeplitz real matrices T gave useful quadrature oscillators by the recurrence x_{n+1}=T x_n. There, we computed their eigenvalues and solved the recurrence in terms of them. Of course, there are many other ways to approach this problem. Here we look at another approach that gives a good geometric picture of what happens, can be applied to general 2\times 2 matrices, and may be used as a starting point for the n\times n case.

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A look at a new digital quadrature oscillator

Two sinusoidal signals are said to be in quadrature if they have a constant phase difference of 90º. Quadrature signals are widely used in signal processing. A digital quadrature oscillator is just an algorithm that computes the sequence x_n = (\cos(\omega n), \sin(\omega n)), n\geq 0, or a similar sequence of sinusoids in quadrature. Here \omega is the oscillator frequency in radians per sample. Direct computation of this sequence is very time consuming, because the trigonometric functions have to be evaluated for each sample. Therefore, it is a good idea to use a linear recurrence scheme to compute x_n. Using basic trigonometric identities, we see that

x_{n+1} = A x_n,\quad x_0=\begin{pmatrix}1\\0\end{pmatrix},

with

A = \begin{pmatrix}\alpha_1 & -\alpha_2\\\alpha_2 & \alpha_1\end{pmatrix},\quad \alpha_1 = \cos(\omega),\ \alpha_2=\sin(\omega).

However, to actually perform these computations in a digital processor, one has to quantize \alpha_1,\alpha_2, meaning that one has to replace \alpha_1,\alpha_2 by approximations. It is easy to see that if one replaces \alpha_1,\alpha_2 by some perturbation, then the eigenvalues of A are no longer in the unit circle, so the oscillation can grow or decay exponentially and one would need to apply an AGC scheme to keep this method stable.

Here we will look at a new quadrature oscillator by Martin Vicanek that has appeared recently and solves this problem.

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