Why use this app? It makes it easy to slog through lots of recording files, looking for interesting signals. Load a file, and a waterfall for the entire file is created. You can scroll around, and if you see anything that looks interesting, you can drag select it, and then demodulate it. You can even save the demodulated audio as a WAVE file, that you can listen to later, send to someone else, or play into your digital decoding software, if it is an RTTY, SSTV, etc. transmission.
Support for other SDR recording file formats is possible, you'll need to work with me by providing sample files and details on the format. This program is presently for macOS only. Support for Windows may happen... stay tuned!
NooElec have recently released a new LNA + filter combo called the "SAWbird+ H1 Barebones" which significantly lowers the entry bar for new amateur radio astronomers. It's designed to be used with RTL-SDR or other SDRs for radio astronomy, and in particular reception of the Hydrogen line.
The filter is centered at 1.42 GHz with a 70 MHz bandpass region. The LNA has a minimum gain of 40dB. For hydrogen line observations it is important that the LNA have very low noise figure, and this LNA fits the bill with a ~0.5dB to ~0.6dB noise figure. An additional feature on the PCB is an RF switch that is electrically controlled via expansion headers. This switch allows you to switch out the LNA for a 50 Ohm reference which is useful for calibration in more serious radio astronomy work.
This LNA draws 120mA of current meaning that it will work with the RTL-SDR V3 and Airspy's bias tee, but probably not with the SDRplay's bias tee which is limited to 100mA and seems to trip a fuse at higher current draws. For an SDRplay you could use external power instead, although you will need an additional DC blocking cap to prevent power from entering the SDR and destroying the ESD diodes.
If you don't know what the Hydrogen line is, we'll explain it here. Hydrogen atoms randomly emit photons at a wavelength of 21cm (1420.4058 MHz). Normally a single hydrogen atom will only very rarely emit a photon, but space and the galaxy is filled with many hydrogen atoms so the average effect is an observable RF power spike at 1420.4058 MHz. By pointing a radio telescope at the night sky and integrating the RF power over time, a power spike indicating the hydrogen line can be observed in a frequency spectrum plot. This can be used for some interesting experiments, for example you could measure the size and shape of our galaxy. Thicker areas of the galaxy will have more hydrogen and thus a larger spike. You can also measure the rotational speed of our galaxy by noting the frequency doppler shift.
Although this LNA lowers the entry bar, in order to receive the Hydrogen line with the SAWBird+ H1 you will still need a ~1m+ satellite dish and a feed tuned to 1.42 GHz or high gain Yagi, horn or helical antenna. Antennas and feeds like this are not yet available off the shelf, but if you search our blog for "hydrogen line" you'll see many project examples.
Over on YouTube user [Radio Electronics] has uploaded a useful video showing how to install your own personal SDRplay or RTL-SDR based WebSDR for QO-100 (aka Es'Hail-2) reception. Es'Hail-2 is the first geostationary satellite with amateur radio transponders on board, and is positioned at 25.5°E which covers Africa, Europe, the Middle East, India, eastern Brazil and the west half of Russia/Asia.
The idea behind a WebSDR is to run your RTL-SDR QO-100 receiver on a remote Raspberry Pi (perhaps mounted close to the antenna on your roof etc). The Pi runs custom WebSDR software that has been created from scratch by [Radio Electronics] specifically for monitoring Es'Hail-2. Then you can access your QO-100 receiver from any device on your network that has a web browser (computer/phone/tablet etc). The interface of his WebSDR appears to be quite slick, which multiple QO-100 specific options and labels.
Quite a lot of work must have gone into this software which looks to be of high quality, so it is definitely worth checking out if you are interested in QO-100/Es'Hail-2 monitoring.
In the first video he first talks about various methods for downconverting the 10489.550 MHz QO-100 CW signal into a range receivable by the RTL-SDR or SDRplay. He then goes on to show the exact steps to install and run his WebSDR software on a Raspberry Pi 3.
In the second video he goes on to demonstrate the web browser interface highlighting the QO-100 specific features that he has implemented such as being able to compensate for any LNB frequency drift via a feature that can lock to the QO-100 PSK beacon.
We have just released an updated version of the KerberosSDR Android direction finding app. If you didn't already know KerberosSDR is our experimental 4x Coherent RTL-SDR product. With it, coherent applications like radio direction finding (RDF) and passive radar are possible. Together with the KerberosSDR direction finding Android app it is possible to visualize the direction finding data produced by a KerberosSDR running on a Pi3/Tinkerboard.
The KerberosSDR hardware is currently in preorder status on Indiegogo for the second production batch, and we expect it to be ready to ship out this month. If you preorder then you'll be able to purchase a KerberosSDR at a reduced price of USD$130. After shipping for batch two begins the price will rise to USD$150.
The new version of the KerberosSDR Android app adds the following features:
Heatmap Grid Plotting
Precise TX location pinpointing when enough data points are gathered
Turn by turn navigation to the RDF bearing direction / TX location
Bearing moving average smoothing
To understand what these features are, we've released two demo videos showing them in action. In the first video we use the new features to find an 858 MHz TETRA transmitter, and in the second video we find a 415 MHz DMR transmitter. The first video explains the new features so we recommend watching that first.
KerberosSDR Radio Direction Finding: Heatmap + Auto Navigation to Transmitter Location Demo 1
KerberosSDR Radio Direction Finding: Heatmap + Auto Navigation to Transmitter Location Demo 2
Over on YouTube Corrosive from the SignalsEverywhere channel has uploaded a new video showing us how you can make a DIY upconverter using a HackRF as a signal source and a cheap $10 RF Mixer. An upconverter converts lower frequencies into higher frequencies. For example, an upconverter is commonly used to convert HF signals into VHF, so that VHF/UHF only SDRs can receive HF.
In the video he uses the HackRF as a local oscillator source, a cheap RF mixer on a breakout board, and an Airspy as the receiver. In most circumstances if you needed and upconverter you'd just purchase one like the Ham-it-up, or the Spyverter for ~$40. However the interesting advantage of using a versatile signal generator like the HackRF is that it results in an upconverter that can upconvert HF to almost any frequency. Even without any filtering (which is recommended to remove signal images), Corrosive fings that he has excellent HF reception.
This video is an excellent way to learn about how upconverters work.
HackRF and RF Mixer = DIY RTL SDR Up-converter | Basics of the Passive ADE Mixer
The process to install an RTL-SDR dongle on Windows involves the simple step of running Zadig and installing the generic WinUSB drivers to the RTL-SDR, which shows up as "Bulk-In, Interface (Interface 0)" in Zadig.
However we find that people sometimes accidentally use Zadig to install WinUSB to "Bulk-In, Interface (Interface 1)" by mistake. Installing WinUSB to this interface can break your installation, and it can cause the RTL-SDR to display a "usb_open error -12" on command line software, and can cause problems connecting to the device on GUI software like SDR#.
Over on YouTube Corrosive from the SignalsEverywhere YouTube channel uploaded a very useful video that shows how to fix this problem.
With so many independent people receiving weather satellite images from the NOAA satellites daily, an interesting collaborative task is to stitch these images together to create a wide area composite image. Fortunately the WXtoIMG software already has stitching as a feature.
We also wanted to provide a brief update on some weather satellites that we RTL-SDR users often receive.
NOAA 15: About two weeks ago NOAA 15 failed and was producing glitched images. However after a few days it came right again, only to have failed again at the end of last month. It appears that the camera scanning motor is getting stuck due to being low on lubricant as the satellite is now well past it's intended life cycle at 11 years old. If you're interested, some info on how the camera on these satellites works can be found here. There is currently no plan for a fix, the only hope is to wait and see if the motor unsticks.
Meteor M2-1: Meteor M2-1 has also recently suffered problems yet again with it's orientation control, and we're regularly seeing off-axis or distorted images that show the curvature of the earth. Over the weekend it was turned off, and should be reset this week. This problem seems to occur and be fixed often, so hopefully it will be back online soon.
Meteor M2-2: The recently launched Meteor M2-2 is functional, but it is still in the testing phase, so is sometimes being turned off. Do not be alarmed if no signal is received sometimes.
GOES-17: GOES-17 is reported to be experiencing problems with it's infrared camera due to a blocked heatpipe, however it appears that they are able to work around this issue and obtain 97% uptime.
Every device that transmits radio waves has a unique and identifiable RF fingerprint which occurs due to the very slightly variations in the hardware manufacturing process. This means that devices using identical transmitters of the same make and model can still be differentiated from one another.
In order to recognize the minute differences in the RF fingerprints of different devices Nihal notes that a good pattern detection algorithm is required, and that a deep learning neural network fits the bill. Using neural network software Tensorflow, and an RTL-SDR for signal acquisition, he was able to train a proof of concept neural model that was able to classify two test transmitters with 97% accuracy.
In the past we've seen similar experiments by Oona Räisänen who used an RTL-SDR to fingerprint several hand held radios heard on the air via small variances in the power and frequencies of each radio's CTCSS tone. Using simple clustering techniques she was able to determine exactly who was transmitting based upon the unique CTCSS.