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.
2021
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]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/854024
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