Multiverse Computing partnered with a European defense entity to deliver a fast, reliable radar signal classification capability that runs in milliseconds on ultra-light edge hardware. The project combined deep learning with advanced electromagnetic modelling to produce a custom Multiverse Computing model that outperforms state-of-the-art benchmarks across all signal-to-noise ratios, even under extreme noise conditions where conventional approaches break down.
The Challenge
The client required fast and reliable radar signal classification under highly noisy conditions, with millisecond inference and a footprint compatible with photonic or ultra-light edge hardware. State-of-the-art models were too slow to train, too heavy to deploy and too sensitive to noise to be operationally viable. The mission context demanded a custom approach that combined deep learning with the physics of electromagnetic signals, rather than an off-the-shelf detection architecture.