Tagged: rtl2832u

SignalsEverywhere Reviews our RTL-SDR Blog L-Band Active Patch Antenna Kit

Over on the SignalsEverywhere YouTube channel Corrosive from the SignalsEverywhere channel has uploaded a review of our RTL-SDR Blog L-Band Active Patch antenna. Our patch antenna can be used for applications such as Inmarsat, Iridium and GPS reception. 

In the video Corrosive shows what the kit comes with, and first demonstrates the antenna working indoors. He also shows how signal SNR can be improved for indoor reception simply by adding a larger ground plane to the back of the antenna and clamping it on with the mounting screw. Later he shows what reception is like outdoors, and shows it being used to decode from STD-C Inmarsat and Iridium signals.

If you're interested in this antenna we also previously posted about TechMinds review video.

The antenna is available for sale on our web store, or from Amazon.

RTL SDR Blog L-Band Inmarsat/Iridium Satellite Service Patch Antenna

Coole-Radar: A Retro Terminal Based Radar Display for ADS-B Aircraft Data

John Wiseman has been working on a cool old-school retro styled aircraft ADS-B radar that runs entirely within a terminal window. So no GUI desktop should be required. The project, called "coole-radar", is available as open source code on GitHub.

It takes decoded ADS-B data via a Virtual Radar Server webpage, so it should be fairly easy to set up together with an RTL-SDR and dump1090 that feeds Virtual Radar Server. The latest version displays a radar screen with decay-like effect, a list of currently detected aircraft, and a pixelated screen of the aircraft image downloaded from the internet.


A Homebrew All-In-One RTL-SDR with Screen and Control Knobs Running on a Mini PC

Over on YouTube user Pablo Sala (KI7OJL) has uploaded a video that shows a neat all-in-one receiver build based on an RTL-SDR. Pablo's build runs on a Pipo x8 Mini PC which is a US$110 PC/tablet that includes a build in LCD touch screen. The build also adds several Arduino powered control knobs for tuning, mode and bank selection, squelch and volume to the base. The knobs directly interface with HDSDR, his chosen software.

The video titles are dated 2017, but the video only seems to have been uploaded recently. Unfortunately we weren't able to find much more information about this build, other than the video.

Homebrew: RTL-SDR Receiver with Arduino-powered knobs on a Pipo X8 Mini PC running HDSDR, May 2017

Networked Radio Direction Finding with KerberosSDR and RDFMapper

We've just uploaded a short Python script to GitHub that allows radio direction bearings from a KerberosSDR to be used with the RDF Mapper software created by Jonathan Musther. RDF Mapper is a (~US$25) program that was initially written for the RDF42, a kit based doppler direction finding system. RDFMapper runs on Windows/MacOS and Linux.

KerberosSDR is our experimental 4-Tuner Coherent RTL-SDR product made in collaboration with Othernet. It can be used for applications such as radio direction finding and passive radar. Currently it's available for US$149 on the Othernet store.

The RDF Mapper software allows you to upload bearings from multiple devices distributed around a city to a public RDF server, and view all the bearings on any internet connected PC. This can allow you to quickly triangulate the location of a transmitter.

Normally you would use RDFMapper combined with an RDF42 to upload bearings, but we've written a simple script that can be used to upload bearings generated by a KerberosSDR onto the server. The RDFMapper software can then be used to visualize those bearings.

The script is based on Python, and can run directly on the Pi 3/4 or Tinkerboard that is running the KerberosSDR, or on another PC that can see the KerberosSDR bearing server if you prefer.

Instructions are available on the GitHub page. Simply set unique station names for each of your distributed units, entry your lat/lon and fixed direction bearing. Then on the RDF Mapper software open the 'Web upload/download' tab and add the unique station ID name. All the other tabs for connecting to a GPS and serial port can be ignored, as those are used for the RDF42.

This script will only work for stationary KerberosSDR units as the lat/lon is fixed. If you want to try radio direction finding in a vehicle, we recommend using our Android App for a better experience. If there is interest, we may also add support for the Android app to upload to an RDFMapper server for mobile bearing uploads. 

Notes: RDFMapper runs on the system's default browser and it needs to run in either Chrome or Firefox to work. IE does not work. It also appears that Jonathan processes orders manually, so we just want to note that there may be a delay between payment and receiving the software.

RDF Mapper Software. Data from networked units.
RDF Mapper Software. Plotting bearing data from networked units.

TechMinds Reviews our RTL-SDR Blog L-Band Patch Antenna + Horn & Dish Mod

Over on YouTube the TechMinds YouTube channel has uploaded a review of our RTL-SDR Blog L-Band patch antenna which we recently released. TechMinds tests the antenna on a STD-C Inmarsat channel with the Scytale-C decoder, and on various AERO ACARS transmissions with JAERO. Later in the video he also tests the patch antenna on Iridium reception using the Iridium Toolkit software. In all tests the patch is able to suitably receive the signal with either an RTL-SDR or Airspy SDR.

We also wanted to make a note about an additional tip regarding polarization that many people using the antenna seem to have missed. As Inmarsat signals are LHCP polarized, it is important to not only point the antenna towards the satellite, but also to rotate the antenna to match the polarization until maximum SNR is achieved. The rotation can make the difference between strong signals and nothing received at all.

RTL-SDR Active L-Band Patch Antenna For Inmarsat / Iridium / GPS

We've also recently seen a user 'Bert' who has needed to boost the signal strength as he was running the patch inside and at a location in northern Europe with poor reception of Inmarsat. To boost it he simply added a metal horn over the patch made from an old aluminum box, and also a back plate reflector. He notes that this improved his SNR on AERO 10500 from 8 - 9 dB, up to 12 - 14 dB. He also tested using the patch on a dish antenna, and found very good results too.

Aluminum Horn Added to L-Band Patch
Aluminum Horn Added to L-Band Patch
L-Band Patch Antenna on Dish
L-Band Patch Antenna on Dish

cuSignal: Easy CUDA GPU Acceleration for SDR DSP and Other Applications

The RAPIDS cuSignal project is billed as an ecosystem that makes enabling CUDA GPU acceleration in Python easy. Scipy is a Python library that is filled with many useful digital signal processing (DSP) algorithms. The cuSignal documentation notes that in some cases you can directly port Scipy signal functions over to cuSignal allowing you to leverage GPU acceleration.

In computing, most operations are performed on the CPU (central processing unit). However, GPU's (graphical processing units) have been gaining popularity for general computing as they can perform many more operations in parallel compared to CPUs. This can be used to significantly accelerate DSP code that is commonly used with SDRs.

In particular the developers have already created a notebook containing some examples of how cuSignal can be used with RTL-SDRs to accelerate an FFT graph. There are various other DSP examples in the list of notebooks too. According to the benchmarks in the notebooks, the GPU computation times are indeed much faster. In the benchmarks they appear to be using a high end NVIDIA P100 GPU, but other NVIDIA graphics cards should also show a good speedup. 

The cuSignal code is based on CUDA, so for any GPU acceleration code to work you'll need to have an NVIDIA based GPU (like a graphics card) with a Maxwell or newer core.

We note that in the future we'll be investigating how this could be used to speed up the passive radar algorithms that are used in the KerberosSDR. It may also be useful for running DSP code quickly on a $99 NVIDIA Jetson Nano single board computer.

NVIDIA Tesla P100. A high end $3000+ GPU.
NVIDIA Tesla P100. A high end $3000+ GPU.

Creating An Automated Raspberry Pi and RTL-SDR Based NOAA Weather Satellite Station

The nootropicdesign blog has recently uploaded a comprehensive tutorial showing how to create an automated NOAA Weather Satellite ground station using an RTL-SDR V3 and an Raspberry Pi 3. The project also makes use of an Amazon S3 bucket, which is a cheap web storage platform that allows you to store and access the downloaded images.

The tutorial starts by showing you how to set up your Amazon AWS credentials and bucket on the Raspberry Pi, and how to host a simple webpage that can be accessed publicly. The second stage shows how to set up the RTL-SDR drivers and wxtoimg which is used to decode the images. Finally, the third stage shows how to create the automation scripts that automatically schedule a decode, and upload images to the AWS bucket.

Flowgraph for an automated NOAA satellite weather image station.
Flowgraph for an automated NOAA satellite weather image station.

Using an RTL-SDR and Speech To Text to Create Alerts on Specific Phrases

Atlassian Opsgenie Engineer Fahri Yardımcı has recently written up an interesting post that details how he's using Opsgenie and Amazon Transcribe to automatically create alerts when specific voice phrases are mentioned on a radio channel. For example, if the words "blue team" are heard on the radio, the system can automatically issue an alert with the spoken words to members of the blue team in an organization. Amazon Transcribe is a cloud based speech to text service and Opsgenie is a platform that is used for managing and delegating alerts from multiple IT or other computer systems.

The system works by using an RTL-SDR and the ham2mon software to scan, receive and record voice from multiple voice channels. Fahri notes that he modified ham2mon slightly in order to allow it to upload the .wav files to an AWS S3 server which then runs the Amazon Transcribe service to convert the voice into a text file.

To make an interesting use case, we have imagined this scenario: When we detect a phrase in predefined words, like “Help”, “Execute Order 66”, “North outpost is compromised”, “Eggs are boiled”, we want to create an alert in Opsgenie. Opsgenie can send notifications to users via various ways such as push notifications and calls.

Amazon Transcribe uses advanced machine learning methodologies, to convert an audio stream to a text. As mentioned before, ham2mon uploads to .wav files to S3 and a Lambda is triggered from S3 Events. Lambda calls Transcribe API and depending on the result, Lambda creates an Opsgenie Alert through API.

Fahri writes that his system also filters out small files that may just be noise, and files with voice less than 3 second long. He's also added a custom vocabulary to Amazon Transcribe with words commonly heard on the radio, as this improves the transcription algorithm, especially in the presence of radio noise.

The rest of the post goes into further detail about the specific cloud services used and the flow of the system.

Flow Graph of the Radio to Transcription System
Flow Graph of the Radio to Transcription System
An example alert from Opsgenie when the phrase "red team" was heard.
An example alert from Opsgenie when the phrase "red team" was heard.