Last week I presented a JT4G detection algorithm intended to detect very weak signals from DSLWP-B, down to -25dB SNR in 2500Hz. I have now processed the recordings of yesterday’s transmissions with this algorithm and here I look at the results. I have also made a Python script with the algorithm so that people can process their recordings easily. Instructions are included in this post.
In March this year I spoke about the Amateur VLBI with LilacSat-2 experiment. This experiment consisted of a GPS-synchronized recording of LilacSat-2 at groundstations in Harbin and Chongqing, China, which are 2500km apart. The experiment was a preparation for the Amateur VLBI project with the DSLWP lunar orbiting satellites, and I contributed with some signal processing techniques for VLBI.
As you may know, the DSLWP-B satellite is now orbiting the Moon since May 25 and the first Amateur VLBI session was performed last Sunday. The groundstations at Shahe in Beijing, China, and Dwingeloo in the Netherlands performed a GPS-synchronized recording of the 70cm signals from DSLWP-B from 04:20 to 5:40 UTC on 2018-06-10. I have adapted my VLBI correlation algorithms and processed these recordings. Here are my first results.
Last Sunday, I used Scott Tilley VE7TIL’s Doppler measurements of the DSLWP-B S-band beacon to perform orbit determination using GMAT. Yesterday Scott sent me the Doppler data he has been collecting during this week. I have re-run my orbit determination process to include this new data.
Below I show the Keplerian state that was determined on 2018-06-03, in comparison with the new state determined on 2018-06-10 (both are referenced to the same epoch of 2018-05-26 00:00:00 UTC).
This is a follow-up on the series about DSLWP-B’s orbital dynamics (see part I and part II). In part I we looked at the tracking files published by Wei Mingchuan BG2BHC, which list the position and velocity of the satellite in ECEF coordinates, and presented basic orbit propagation with GMAT. In part II we explored GMAT’s capabilities to plan and perform manoeuvres, making a tentative simulation of DSLWP-B’s mid-course correction and lunar orbit injection. Now we turn to the study of DSLWP-B’s elliptical lunar orbit.
In this post we will examine the Keplerian elements of the orbits described by each of the tracking files published so far. We will also use Scott Tilley VE7TIL’s Doppler measurements of the S-band beacon of DSLWP-B to validate and determine the orbit.
This forms parts of a series of posts showing how to use GMAT to track the DSLWP-B Chinese lunar satellite. In part I we looked at how to examine and validate the tracking files published by BG2BHC using GMAT. It is an easy exercise to use GMAT to perform orbit propagation and produce new tracking files. However, note that the available tracking files come from orbit planning and simulation, not from actual measurements. It seems that the elliptical lunar orbit achieved by DSWLP-B is at least slightly different from the published data. We are already working on using Doppler measurements to perform orbit determination (stay tuned for more information).
Recall that there are three published tracking files that can be taken as a rough guideline of DSLWP-B’s actual trajectory. Each file covers 48 hours. The first file starts just after trans-lunar injection, and the second and third files already show the lunar orbit. Therefore, there is a gap in the story: how DSLWP-B reached the Moon.
There are at least two manoeuvres (or burns) needed to get from trans-lunar injection into lunar orbit. The first is a mid-course correction, whose goal is to correct slightly the path of the spacecraft to make it reach the desired point for lunar orbit injection, which is usually the lunar orbit periapsis (the periapsis is the lowest part of the elliptical orbit). The second is the lunar orbit injection, a braking manoeuvre to get the spacecraft into the desired lunar orbit and adjust the orbit apoapsis (the highest part of the orbit). Without a lunar orbit injection, the satellite simply swings by the Moon and doesn’t enter lunar orbit.
In this post we will see how to use GMAT to calculate and simulate these two burns, so as to obtain a full trajectory that is consistent with the published tracking files. The final trajectory can be seen in the figure below.
As you may well know, on May 20 a CZ-4C rocket launched from Xichang, China, to deliver Queqiao, the Chang’e 4 relay satellite, to the Moon. Queqiao is a communications relay satellite designed to orbit the L2 point of the Earth-Moon system, supporting the future Chang’e 4 rover that will land on the far side of the Moon. From the L2 point, Queqiao has a good view of both the Earth and the far side of the Moon.
This launch was shared by the DSLWP-A and -B microsatellites, also called Longjiang 1 and 2. These two satellites are designed to be put on a 200 x 9000km lunar orbit and their main scientific mission is a proof of concept of the Discovering the Sky at Longest Wavelengths experiment, a radioastronomy HF interferometer that uses the Moon as a shield from Earth’s interferences.
The DSLWP satellites carry an Amateur radio payload which consists of a 250 baud (or 500 baud) GMSK transmitter which uses \(r=1/2\) or \(r=1/4\) turbo codes, a JT4G beacon, and a camera allowing open telecommand (such as the camera on BY70-1 and LilacSat-1). A year ago, while the radio system was being designed, I wrote a post about DSLWP’s SSDV downlink, which transmits the images taken by the camera.
Wei Mingchuan BG2BHC, who is part of the DSLWP team, has been posting updates on Twitter about the status of the mission. If you’ve been following these closely, you’ll already know that unfortunately radio contact with DSLWP-A was lost on the UTC afternoon of May 22. Since then, all tries to contact the spacecraft have failed (the team will publicly release more information about its fate soon). On the other hand, DSLWP-B has been successfully injected into lunar orbit and is now orbiting the Moon since the UTC afternoon of May 25.
More posts will follow about the radio communications of DSLWP, but this series of posts will deal with the orbital dynamics part of the mission. In this first post, I will look at the tracking files released so far by Wei, which can be used to compute the spacecraft’s position and Doppler.
Following a discussion with Carlos Cabezas EB4FBZ over on the Spanish telegram group Radiofrikis about using Codec2 with DMR, I set out to study the error correction used in DMR, since it quickly caught my eye as something rather interesting. As some may know, I’m not a fan of using DMR for Amateur Radio, so I don’t know much about its technical details. On the other hand, Carlos knows a lot about DMR, so I’ve learned much with this discussion.
In principle, DMR is codec agnostic, but all the existing implementations use a 2450bps AMBE codec. The details of the encoding and FEC are taken directly from the P25 Half Rate Vocoder specification, which encodes a 2450bps MBE stream as a 3600bps stream. Here I look at some interesting details regarding the FEC in this specification.
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 BG2BHCpublished 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.
In my previous post I talked about the RFC5170 LDPC codes used in Outernet. There I explained in some detail the pseudorandom construction of the LDPC codes and the simple erasure decoding algorithm used both in free-outernet and in the official closed-source receiver.
I also commented that erasure decoding for an LDPC code (or any other linear code) amounts to solving a linear system. This can be done using any algebraic method, such as Gaussian elimination. However, the simple decoding algorithm used in Outernet is as follows: try to find an equation with only one unknown, solve for that unknown, and repeat until the system is solved. Clearly this algorithm can fail even if the system can be solved (see my previous post for some examples and formal results). I will refer to this algorithm as iterative decoding, as it is done in the RFC.
With these two things in mind, I wondered about the performance of the LDPC codes used in Outernet and the iterative decoding algorithm. I’ve done some simulations and here I present my results.