DeepAoANet: Learning angle of arrival from software defined radios with deep neural networks
Direction finding and positioning systems based on RF signals are significantly impacted by multipath propagation, particularly in indoor environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle of Arrival (AoA) in the presence of multipath or when operating in a weak signal r...
المؤلفون الرئيسيون: | Dai, Z, He, Y, Tran, V, Trigoni, N, Markham, A |
---|---|
التنسيق: | Journal article |
اللغة: | English |
منشور في: |
IEEE
2022
|
مواد مشابهة
-
The angle of arrival estimation of frequency-hopping cooperative object based on software-defined radio
حسب: Rustamaji, وآخرون
منشور في: (2024-04-01) -
MS-ANet: deep learning for automated multi-label thoracic disease detection and classification
حسب: Jing Xu, وآخرون
منشور في: (2021-05-01) -
Autonomous software-defined radio receivers for deep space applications /
حسب: Hamkins, Jon, 1968-, وآخرون
منشور في: (2006) -
MIB-ANet: A novel multi-scale deep network for nasal endoscopy-based adenoid hypertrophy grading
حسب: Mingmin Bi, وآخرون
منشور في: (2023-04-01) -
DeepDeMod: BPSK Demodulation Using Deep Learning Over Software-Defined Radio
حسب: Arhum Ahmad, وآخرون
منشور في: (2022-01-01)