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.