Electronic Warfare Products
We create products for the DoD's most difficult Electronic Warfare (EW) and Radio Frequency (RF) Challenges. For additional options and pricing, send an e-mail to firstname.lastname@example.org
TReX is a better way to experiment, train, and plan next generation SIGINT & EW operations. It is an easy to set up hardware and software suite that enables users to conduct true testing and evaluation of EW systems and techniques anytime and anywhere. TReX can be easily set up in a lab, classroom, outdoor range, or deployed environment. It acts as a “victim” system that enables users to transmit and receive high fidelity representations of many commercial and non-commercial threat emitters, and test new EW techniques against their waveforms. Users are able to observe the effects of applied EW systems and techniques on the simulated victim radios through the TReX application.
TReX can also be used to practice CONOPs in a network simulated environment. Several TReX devices can be set up to communicate with one another and the generated reports can be used to enable better planning by showing commanders which techniques are most effective against threat receivers. TReX currently has the capability to mimic the following specific threat radio waveforms:
- Fast Frequency Hopping Tactical Radios
- Fixed Frequency Tactical Radios
- GNSS Signals
- TDMA/CDMA Bulk Transmitters
- Modern Comms Modulations including FSK, BPSK, BPSK-DSSS, OFDM, and QAM
Galileo is a software solution that performs unsupervised Machine Learning to target adversary frequency-hopping tactical radios, isolate frequencies and hop patterns, and perform automatic predictive EW response.
Galileo uses our FINED (Fused INternals and Externals Detection) algorithm to blindly detect, characterize, and infer EW targeting parameters with no static target library.
Galileo labels known threats in near real-time to provide a continuously updating training set to its externals-based detectors. Galileo not only detects fast frequency hopping signals with no prior information, but also identifies small observed variances in known threat emitters. It detects individual threat hops, clusters multiple emitters simultaneously, and in most cases automatically demodulates and frame-synchronizes anomalous emitters.