Deep learning clutter suppression improves multistatic ISAC target estimates
Residual and attention networks beat conventional baselines on position and velocity under dense dynamic clutter.
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Residual and attention networks beat conventional baselines on position and velocity under dense dynamic clutter.
Researchers propose IIns-GAN, a GAN that synthesizes realistic labeled ultra-wideband wireless signals, cutting dataset costs for wireless sensing training.
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