Tagged: artificial intelligence

Automatic Signal Recognition with AI Machine Learning and RTL-SDR

Thank you to Trevor Unland for submitting his AI machine learning project called "RTL-ML" which automatically recognizes and classifies eight different signal types on low-power ARM processors running an RTL-SDR.

Trevor's blog post explains the machine learning architecture in detail, the accuracy he obtained, and how to try it yourself. If you try it for yourself, you can either run the pre-trained model or train your own model if you have sufficient training data.

The code is entirely open source on GitHub, and the training set data has been shared on HuggingFace. 

RTL-ML is an open-source Python toolkit for automatic radio signal classification using machine learning. It runs on ARM single-board computers like the Raspberry Pi 5 or Indiedroid Nova paired with an RTL-SDR Blog V4, achieving 87.5% accuracy across 8 real-world signal types including ADS-B aircraft transponders, NOAA weather satellites, ISM sensors, FM broadcast, NOAA weather radio, pagers, and APRS.

The project provides a complete pipeline from signal capture to trained classifier. Unlike academic approaches that rely on synthetic data or expensive GPU hardware, RTL-ML uses real signals captured from actual antennas and runs entirely on edge hardware with no cloud dependency. The Random Forest model is 186KB and processes signals in around 120ms on a Pi 5.

The GitHub repository includes the full capture and training scripts, a pre-trained model, 8 validated spectrograms, and documentation for adding new signal types. It works out of the box on both Raspberry Pi 5 and Indiedroid Nova with identical code and accuracy.

RTL-ML Setup: RTL-SDR Blog V4, Dipole Antenna and Indiedroid Nova ARM Computer.
RTL-ML Setup: RTL-SDR Blog V4, Dipole Antenna and Indiedroid Nova ARM Computer.

You might also be interested in some similar projects we've posted about in the past, such as this Shazam-style signal classifier, which used audio data from sigidwiki.com, and an Android app doing the same thing (which unfortunately now appears to have been removed from Google Play). There is also this deep learning based signal classifier model.

GhostHunter (Anti-LIF): Using Spiking Neural Networks to Rescue Satellite Signals Drowned in Noise

Thank you to Edwin Temporal for writing in and showing how his proprietary neuromorphic engine, GhostHunter (Anti-LIF), is being used to recover satellite data buried in the noise floor, which typical DSP methods would fail to do.

To recover the signals, Edwin uses trained Spiking Neural Networks (SNN). SNNs are artificial neural networks that draw further inspiration from nature by incorporating the 'spiking' on/off behavior of real neurons. Edwin writes:

My engine has successfully extracted and decoded structured data from high-complexity targets by mimicking biological signal processing:

Technosat: Successful decoding of GFSK modulations under extreme frequency drift and low SNR conditions.

MIT RF-Challenge: Advanced recovery of QPSK signals where traditional digital signal processing (DSP) often fails to maintain synchronization.

These missions are fully documented in the https://temporaledwin58-creator.github.io/ghosthunter-database/, which serves as a public ledger for my signal recovery operations. Furthermore, the underlying Anti-LIF architecture is academically backed by my publication on TechRxiv, proving its efficiency in processing signals buried deep within the noise floor.

Although the engine remains proprietary, I provide comprehensive statistical reports and validation metrics for each mission. I believe your audience would be thrilled to see how Neuromorphic AI (SNN) is solving real-world SIGINT challenges.

In the database, Edwin shows how his Anti-LIF system has recovered CW Morse code telemetry and QPSK data from noisy satellite signals. 

While Edwin's Anti-LIF is proprietary, he is offering proof of concept decoding. If you have a 250MB or less IQ/SigMF/Wav recording of a signal that is buried in the noise floor, you can submit it to him via his website, and he will run Anti-LIF on it for analysis.

Advanced readers interested in AI/neural network techniques for signal recovery can also check out his white paper on TechRxiv, where he shows signal recovery from signals buried in WiFi noise, as well as results from use in ECG and Healthcare applications.

An Example Signal Recovery with the Anti-LIF Spiking Neural Network
An Example Signal Recovery with the Anti-LIF Spiking Neural Network

AI Cloud Detection for GOES Weather Satellite Images on a Raspberry Pi

Over on his blog account at Hackser.io Justin Lutz has uploaded an article describing how he uses AI object detection to automatically detect clouds on weather satellite images that he's downloaded from GOES satellites via an RTL-SDR.

Lutz's blog post first describes and shows his RTL-SDR GOES reception setup. Then, it explains how he used Edge Impulse on his Raspberry Pi 4 to create an AI model that automatically detects the clouds in the image.

The process begins by importing 100 images into Edge Impulse, manually labelling the clouds in each image, training the model, and testing it. The result was an average detection accuracy of 90%.

Proposing a Software Defined Radio based “AI Battle Buddy”

Over on YouTube, Isaac Botkin of TREX LABS has uploaded a video discussing how he proposes to build an "AI Battle Buddy" with a built-in software-defined radio. The idea is to combine a wide frequency range software-defined radio with AI tools that automatically determine and alert the device owner when something interesting occurs in the radio spectrum.

Isaac gives example use cases for the device, such as alerts when jamming is detected, drone detection alerts, alerts when there is suddenly increased public safety radio traffic or if there are nearby public safety radio transmissions, and information about nearby aircraft and NOAA weather alerts.

The device is proposed to have no screen, but would simply give audio alerts via Bluetooth earpiece, or text alerts via smartphone or smart watch. 

Ultimately, such a device has yet to be built for the general consumer market, but Isaac notes that AI-SDR devices like the Anduril Pulsar already exist for the military consumer.

How to Make an AI Battle Buddy for Electronic Warfare

Real Time Speech to Text from Radio Speech via DragonOS, SDR4Space, Mosquitto and WhisperCPP

Real time high quality speech to text is now possible with OpenAI's WhisperCPP, a high-performance and open source automatic speech recognition model.

In his latest video on YouTube, Aaron demonstrates how to use his latest DragonOS image to transcribe audio from a radio voice channel that is received with an RTL-SDR. He makes use of SDR4Space as the command line receiver, WhisperCPP as the AI transcriber and Mosquitto for monitoring WhisperCPP outputs and displaying the text to the terminal.

Here's a short video showing exactly how to setup and run SDR4space in such a way that real time IQ captures are demodulated and feed to WhisperCPP (High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model) for transcribing.

The latest DragonOS FocalX R28 comes w/ everything needed to do exactly what I show in this video, to include a sample tiny model.

You'll noticed in the video that jobs are placed in a queue for continued captures and results are also sent over to Mosquitto MQTT where a client can see messages as they are created.

I chose to use an RTLSDR v3 dongle for the capture, but it's possible to configure SDR4space to use a variety of soapy supported SDRs.

In his first video Aaron shows how to get setup with the system on DragonOS. Shortly after uploading his first tutorial, Aaron noticed that recompiling WhisperCPP on the local system yielded a significant decrease in the processing time of the AI. After recompiling locally the transcribing then became near real time. In the second video Aaron briefly demonstrates the real time transcription. 

DragonOS FocalX Capture and Transcribe IQ w/ SDR4space/WhisperCPP/Mosquitto (RTLSDR, OpenAI)

DragonOS FocalX Captured IQ to Text Faster w/ SDR4space/WhisperCPP/Mosquitto (RTLSDR)

In the past we posted a similar project that was based on the Amazon Transcribe cloud service. However WhisperCPP runs on a local machine, is open source and seems to be at least as good as Amazon Transcribe. So this appears to be a significant leap in transcribing ability and we could see it being used to automatically create text logs and alerts based on various radio channels.

DARPA Spectrum Collaboration Challenge $2 Million Dollar Championship Video

DARPA (Defense Advanced Research Projects Agency) has recently released video from their Spectrum Collaboration Challenge Championship Event where team GatorWings took home a two million dollar prize. In the original DARPA grand challenge teams competed to produce an autonomous car that can get through an obstacle course. In this spectrum challenge DARPA poses the questions, what if there was no FCC to control the band plan, and how do we make more efficient use of a scarce spectrum?

Given those questions the goal is for software defined radios driven by artificial intelligence's created by each team to autonomously find ways to manage and share the spectrum all by themselves. The AI's are required to find ways to listen and learn the patterns of other AI SDRs using differing wireless standards all of which are competing for the same slice of spectrum at the same time. The competition asks the AI's to provide simulated wireless services (phone calls, data link, videos, images) during a simulation run with all the AI's running at once. Whichever AI is able to provide the most stable services and at the same time share the spectrum fairly with the other AI's wins.

On October 23, 2019, ten teams of finalists gathered to compete one last time in the Championship Event of DARPA's Spectrum Collaboration Challenge (SC2), a three-year competition designed to unlock the true potential of the radio frequency (RF) spectrum with artificial intelligence. DARPA held the Championship Event at Mobile World Congress 2019 Los Angeles in front of a live audience.

Team GatorWings from University of Florida took home the $2 million first prize, followed by MarmotE from Vanderbilt University in second with $1 million, and Zylinium, a start-up, in third with $750,000.

Throughout the competition, SC2 demonstrated how AI can help to meet spiking demand for spectrum. As program manager Paul Tilghman noted in his closing remarks from the SC2 stage: "Our competitors packed 3.5 times more wireless signals into the spectrum than we're capable of today. Our teams outperformed static allocations and demonstrated greater performance than current wireless standards like LTE. The paradigm of collaborative AI and wireless is here to stay and will propel us from spectrum scarcity to spectrum abundance."

The highlights video is shown below, and the full two hour competition stream can be viewed here. 

Highlights from the Spectrum Collaboration Challenge Championship Event

The competition was run on the DARPA Colosseum, the worlds largest test bed for performing repeatable radio experiments. Capable of running up to 128 two channel software defined radios with 3 peta-ops of computing power it allows experimenters to accurately simulate real world RF environments. It works by connecting special "channel emulator" RF computing hardware to each physical SDR, which can emulate any RF environment.

Artificial Intelligence Radio – Transceiver Now Released for Crowdfunding

Last week we posted about the Artificial Intelligence Radio - Transceiver (AIR-T), which was awaiting release for crowdfunding. Today the Crowd Supply campaign for it has gone live. 

As expected, the AIR-T is not a cheap with it coming in at US$5,699, and this is with a 10% discount off the MSRP. However, the AIR-T is likely to be more of interest to high end industry and university researchers who have research money to spend. Also, compared to Ettus E310/N310 and LimeNET Mini SDRs which have built in non-GPU based computing platforms and similar SDR performance, the AIR-T could be seen as reasonably priced assuming that the software and drivers for it are decent. In the future we expect to see the price of similar SDR-AI development boards eventually reduce down to hobbyist level prices. 

The basic idea behind the AIR-T is to combine a 2x2 MIMO SDR transceiver with a NVIDIA Jetson TX2 GPU that can be used to run artificial intelligence (AI) software fast. They will include software that will allow GNU Radio and Python code to be easily ported to the GPU architecture. 

Why build tomorrow’s tech with yesterday’s signal processing tools? The Artificial Intelligence Radio - Transceiver (AIR-T) is a fully integrated, single-board, artificial intelligence equipped, software defined radio platform with continuous frequency coverage from 300 MHz to 6 GHz. Designed for new engineers with little wireless experience to advanced engineers and researchers who develop low-cost AI, deep learning, and high-performance wireless systems, AIR-T combines the AD9371 RFIC transceiver providing up to 2 x 2 MIMO of 100 MHz of receiving bandwidth, 100 MHz of transmitting bandwidth in an open and reprogrammable Xilinx 7 FPGA, with fast USB 3.0 connectivity.

The AIR-T has custom and open Ubuntu software and custom FPGA blocks interfacing with GNU Radio, allowing you to immediately begin developing without having to make changes to existing code. With 256 NVIDIA cores, you can develop and deploy your AI application on hardware without having to code CUDA or VHDL. Freed from the limited compute power of a single CPU, with AIR-T, you can get right to work pushing your telecom, defense, or wireless systems to the limit of what’s possible.

The Artificial Intelligence Receiver - Transceiver (AIR-T) SDR
The Artificial Intelligence Receiver - Transceiver (AIR-T) SDR

The Artificial Intelligence Radio – Transceiver

Over on Crowd funding site Crowd Supply, a new SDR product is currently awaiting release of its crowd funding stage. The proposed product is called the AIR-T, which stands for Artificial Intelligence Radio - Transceiver. The basic idea behind the board is to combine a 2x2 MIMO SDR transceiver with a NVIDIA Jetson TX2 GPU that can be used to run artificial intelligence (AI) software fast.

The SDR transceiver chip used is a Analog Devices 9371. This is a high end chip that can be found on high end SDR hardware like USRPs. If you're interested we had a post about decapping the AD9361 recently, which is a similar chip. It provides 2x2 MIMO channels, with up to 100 MHz RX bandwidth and 250 MHz TX bandwidth. The NVDIA Jetson TX2 is a GPU 'supercomputer' module specifically designed for AI processing. Many AI/machine learning algorithms, such as neural networks and deep learning run significantly faster on GPU type processors when compared to more general CPU's.

These are not cheap chips with the AD9371 coming in at over US$250 each, and the Jetson TX2 coming in at US $467. Although we don't know what sort of bulk discounts the AIR-T manufactures could get. But it will be certain that the AIR-T will not be for the budget minded.

The board is still awaiting release of it's crowdfunding round, and you can sign up to be notified of when the project launches on their Crowd Supply page.

The melding of AI and the RF spectrum will be common in the future, and a development board like this is one of the first steps. Some of the interesting use cases that they present are pasted below:

Wireless

From Wi-Fi to OpenBTS, use deep learning to maximize these applications. By pairing a GPU directly with an RF front-end it eliminates the need of having to purchase an additional computer or server for processing. Just power the AIR-T on and plug in a keyboard, mouse, and monitor and get started. Use GNURadio blocks to quickly develop and deploy your current or new wireless system. For those who need more control, talk directly with the drivers using Python or C+. And for those superusers out there, the AIR-T is an open-platform, so you can program the FPGA and GPU directly.

Satellite Communications

Communicating past Pluto is hard. With the power of a single-board SDR with an embedded GPU, the AIR-T can certainly prove out concepts before you launch them into space. Reduce development time and costs by adding deep learning to your satellite communication system.

Ground Communications

There is an endless number of terrestrial communication systems with more being developed every day. As the spectral density becomes more congested, AI will be needed to maximize these resources. The AIR-T is well-positioned to easily and quickly help you prototype and deploy your wireless system.

Video/Image/Audio Recognition

The AIR-T allows you to demodulate a signal and apply deep learning to the image, video, or audio data in one integrated platform. For example, directly receiving a signal that contains audio and peforming speech recognition previously required multiple devices. The AIR-T integrates this into one easy to use package. Whatever your application is, from speech recognition to digital signal processing, the integrated NVIDIA GPU will jump start your applications.

Pattern Recognition

For many communications and radar applications once the signal is collected it must be sent to an off-board computer for additional processing and storage. This consumes valuable time. The AIR-T eliminates this. From its inception, it was designed to process signals in real-time and eliminate unnecessary latency.