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Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion
This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy.
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion
The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging.
IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays
We utilize a recurrent neural network structure to parameterize the IHT algorithm.
Widely Separated MIMO Radar Using Matrix Completion
We present a low-complexity widely separated multiple-input-multiple-output (WS-MIMO) radar that samples the signals at each of its multiple receivers at reduced rates.
AETomo-Net: A Novel Deep Learning Network for Tomographic SAR Imaging Based on Multi-dimensional Features
To address them, in this paper, a novel imaging network (AETomo-Net) based on multi-dimensional features is proposed.
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