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- W4207066166 abstract "Location-awareness technologies are fast becoming key instruments in civil applications and military sectors. High-accuracy localization systems can be empowered through equip-ping antenna arrays as they can provide fine angle-of-arrival estimations. However, obstacles in the environment can lead to signal non-line-of-sight propagation between devices, resulting in large estimation errors. In this paper, we provide a deep learning approach to achieve angle-of-arrival estimation in indoor complex environments based on ultra-wide bandwidth systems. In the proposed approach, we present a residual network to solve the degradation problem and accomplish the regression task. Instead of relying on handcraft waveform features, we take the amplitude of the channel impulse response and the phase-of-arrival as model input to fully extract the contained information. Our work is validated by an extensive indoor measurement campaign with FCC-compliant ultra-wide bandwidth radios. Results show that our approach outperforms conventional machine learning approaches in practical scenarios." @default.
- W4207066166 created "2022-01-26" @default.
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- W4207066166 date "2021-12-01" @default.
- W4207066166 modified "2023-10-02" @default.
- W4207066166 title "A Deep Learning Based AoA Estimation Method in NLOS Environments" @default.
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- W4207066166 doi "https://doi.org/10.1109/gcwkshps52748.2021.9682146" @default.
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