Adaptive LPD Radar Waveform Design with Generative Deep Learning
IEEE Transactions on Radar Systems — 2025
We propose a learning-based method for adaptively generating low probability of detection (LPD) radar waveforms that blend into their operating environment. Our waveforms are designed to follow a distribution that is indistinguishable from the ambient radio frequency (RF) background—while still being effective at ranging and sensing. To do so, we use an unsupervised, adversarial learning framework; our generator network produces waveforms designed to confuse a critic network, which is optimized to differentiate generated waveforms from the background. To ensure our generated waveforms are still effective for sensing, we introduce and minimize an ambiguity function-based loss on the generated waveforms. We evaluate the performance of our method by comparing the single-pulse detectability of our generated waveforms with traditional LPD waveforms using a separately trained detection neural network. We find that our method can generate LPD waveforms that reduce detectability by up to 90% while simultaneously offering improved ambiguity function (sensing) characteristics. Our framework also provides a mechanism to trade-off detectability and sensing performance.
@article{ Ziemann2025Adaptive,
author = { Ziemann, Matthew R. and Metzler, Christopher A. },
title = { Adaptive LPD Radar Waveform Design with Generative Deep Learning },
journal = { IEEE Transactions on Radar Systems },
year = { 2025 },
}