SignalEye – AI Software for Automated Signal Classification

SignalEye Machine Learning RF Radio Signal Software

Automated Spectrum Situational Awareness

General Dynamics SignalEye™ solution provides spectrum situational awareness by automating the classification of signals through the use of machine learning. This electronic warfare software provides tactical warfighters and security personnel with a timely, accurate view of the threat in the RF spectrum. SignalEye is always on - learning and alerting you to the signals that threaten you and your mission.

AI for RF

The RF spectrum has emerged as a new fighting domain. Peer and near-peer adversaries threaten our dominance on land, air, sea, space and cyber every day. Use artificial intelligence to make sense of the vast number of signals you’re seeing and those you’ve collected for true situational awareness. Recognize when a threat is coming for you, not when it’s at your door.

With SignalEye, you will know what is normal and what is a threat at the speed your mission needs.

DETECT, ISOLATE AND CLASSIFY SIGNALS FOR RF SPECTRUM SITUATIONAL AWARENESS

General Dynamics SignalEye uses artificial intelligence (AI) to provide warfighters and security personnel with a timely, accurate view of the threat in the RF spectrum and enables analysts to detect trends in the adversary’s behavior. SignalEye doesn’t require specialized hardware acceleration. In a tactical context, this electronic warfare software deploys on a commodity hardware as an add-on to a RF front end system solution such as iRF’s LiteRail™ or your existing front end. In a classified or unclassified Amazon cloud context, it scales to process petabytes of data.

SignalEye Electronic Warfare AI Software for Automated Signal Classification

Features At A Glance

Machine Learning – signal classification using convolutional neural networks (CNN)
Data Driven – detection capabilities based on neural network training
Streaming – signal detection in streaming digital RF data
Software Only – solution runs on general purpose computer
Hardware Independent – RF front-end agnostic
Mission Independent – integrates with existing user-focused mission interfaces
Standards Based – supports VITA-49, VITA Radio Transport
Public API – C/C++, Python, Java, Scala

SignalEye Radio frequency RF artificial intelligence machine learning software

RF Signal Types

Amplitude Modulation (AM) - Single-Sideband Suppressed-Carrier (SSB-SC-AM), Double-Sideband Suppressed-Carrier (DSB-SC-AM)
Frequency Modulation (FM)
Amplitude and Phase-Shift Keying (APSK) - APSK-16, APSK-32
Amplitude-Shift Keying (ASK) - ASK-2, ASK-4, ASK-8
Continuous Phase Modulation (CPM)
Continuous-Phase Frequency-Shift Keying (CPFSK) - CPFSK-2, CPFSK-4, CPFSK-8
Gaussian Minimum Shift Keying (GMSK)
Frequency-Shift Keying (FSK) - FSK-2, FSK-4, FSK-8
Orthogonal Frequency-Division Multiplexing (OFDM)
Phase-Shift Keying (PSK) - 8PSK
Binary Phase-Shift Keying (BPSK)
Quadrature Phase-Shift Keying (QPSK) - General QPSK , π/4-QPSK
Quadrature Amplitude Modulation (QAM) - QAM-8, QAM-16, QAM-128*, QAM-256*

SignalEye in Context - AI Software For Modulation Recognition

SignalEye And Your Electronic Warfare (EW) System

Metadata provided by SignalEye:

Modulation Type
Center Frequency
Bandwidth
Signal-to-Noise Ratio (SNR) in dB
Capture Start and Stop Time
Capture Duration in Milliseconds
Capture Time Offset in Milliseconds
Neural Network Confidence

Download SignalEye in Context Brochure

Frequently Asked Questions

Using SignalEye
RF Context for SignalEye

Technical Requirements for SignalEye

What hardware is required for SignalEye? Can I run it on a laptop? In a VM? On a server? On a cluster of servers? In the cloud?

Currently SignalEye runs on a laptop. Please contact us to discuss options for running SignalEye on VMs, servers, clusters of servers, or in the cloud.

What are the system requirements (CPU, memory, storage) for a laptop running SignalEye?

System Requirements

Component	Recommended
Computer	Standard laptop
Display	At least 1920 x 1280 resolution
Processor	Intel Quad-Core i7-7700HQ Processor 2.80 GHz with Turbo Boost up to 3.8 GHz
Memory	32GB DDR4-2400 RAM
Disk Space	2 TB PCIe Solid State Drive Note: Faster disk controllers may improve performance
Video	NVIDIA GeForce GTX 1050
Network Cards, USB, Other	Thunderbolt Ports: Thunderbolt 3 x1 USB Ports: USB 3.1 x2 (Gen 1 Type-A)
Audio	Standard sound card or embedded chipset
Operating System	See below
Programming Environment	See below

Operating System

The SignalEye software requires CentOS 7.5 (64-bit) or greater with the following installation options:
• CentOS Installation Type: Server + GUI
• Package Installation: Default when choosing "Server + GUI"
• Partitioning: See table below.

Partitioning	Size (GB)
/	500
/boot	1
/boot/efi	1
/var	200
/home	500

* Note: any created SWAP partition will be disabled during installation.

What’s the size of the SignalEye executable?

SignalEye is not a single executable, it is a collection of containers synergistically producing a result. Thus, it requires approximately 1.2 GB of disk space.

What operating systems does SignalEye support?

SignalEye works on CentOS 7.5+.

Does SignalEye use FPGAs?

The current version of SignalEye does not use FPGAs. Contact us to learn more about our FPGA plans.

Does SignalEye use GPUs?

The current version of SignalEye does not use GPUs.

How does SignalEye store the signal classification results?

Signal classification results are displayed in the user interface or returned via the API to enterprise data stores. They are not stored.

Can SignalEye be run standalone on a server with communication through standard sockets (TCP/UDP/IP)?

Absolutely. Our communication to SignalEye in streaming mode is over UDP up to 10Gbps.