Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in stepped-frequency radar super-resolution angle-range-doppler imaging. We consider an uncooperative spectrum sharing scenario where the radar is tasked with imaging a sparse scene amidst communication interference that is frequency-sparse due to spectrum underutilization, motivating an 1-minimization problem to recover the radar image and suppress the interference. The problem’s ADMM iteration undergirds the neural network design, yielding a set of generalized ADMM updates with learnable hyperparameters and operations. The network is trained with random data generated according to the radar and communication signal models. In numerical experiments ADMM-Net exhibits markedly lower error and computational cost than ADMM and CVX.
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar / Johnston, Jeremy; Li, Yinchuan; Lops, Marco; Wang, Xiaodong. - In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - ISSN 1053-587X. - 69:(2021), pp. 2818-2832. [10.1109/TSP.2021.3076900]
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar
Lops, Marco;
2021
Abstract
Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in stepped-frequency radar super-resolution angle-range-doppler imaging. We consider an uncooperative spectrum sharing scenario where the radar is tasked with imaging a sparse scene amidst communication interference that is frequency-sparse due to spectrum underutilization, motivating an 1-minimization problem to recover the radar image and suppress the interference. The problem’s ADMM iteration undergirds the neural network design, yielding a set of generalized ADMM updates with learnable hyperparameters and operations. The network is trained with random data generated according to the radar and communication signal models. In numerical experiments ADMM-Net exhibits markedly lower error and computational cost than ADMM and CVX.File | Dimensione | Formato | |
---|---|---|---|
ADMM_published.pdf
solo utenti autorizzati
Tipologia:
Versione Editoriale (PDF)
Licenza:
Accesso privato/ristretto
Dimensione
1.32 MB
Formato
Adobe PDF
|
1.32 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.