Adaptive LPD Radar Waveform Design with Generative Deep Learning
Matthew R. Ziemann Christopher A. Metzler
We propose a novel, 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.