[HTML][HTML] Towards deep radar perception for autonomous driving: Datasets, methods, and challenges
With recent developments, the performance of automotive radar has improved significantly.
The next generation of 4D radar can achieve imaging capability in the form of high …
The next generation of 4D radar can achieve imaging capability in the form of high …
A novel radar point cloud generation method for robot environment perception
Millimeter-wave (mmWave) radar has been widely used in autonomous driving due to its
good performance under harsh weather conditions. In recent years, with the development of …
good performance under harsh weather conditions. In recent years, with the development of …
A machine learning perspective on automotive radar direction of arrival estimation
Millimeter-wave sensing using automotive radar imposes high requirements on the applied
signal processing in order to obtain the necessary resolution for current imaging radar. High …
signal processing in order to obtain the necessary resolution for current imaging radar. High …
Cubelearn: End-to-end learning for human motion recognition from raw mmwave radar signals
mmWave FMCW radar has attracted a huge amount of research interest for human-centered
applications in recent years, such as human gesture and activity recognition. Most existing …
applications in recent years, such as human gesture and activity recognition. Most existing …
A new automotive radar 4d point clouds detector by using deep learning
The millimeter-wave radar, as an important sensor, is widely used in autonomous driving. In
recent years, to meet the requirement of high level autonomous driving applications …
recent years, to meet the requirement of high level autonomous driving applications …
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 (eg MUSIC) …
multipath propagation, particularly in indoor environments. Existing algorithms (eg MUSIC) …
Neural-Network-Based DOA Estimation in the Presence of Non-Gaussian Interference
S Feintuch, J Tabrikian, I Bilik… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of
non-Gaussian, heavy-tailed, and spatially-colored interference. Conventionally, the …
non-Gaussian, heavy-tailed, and spatially-colored interference. Conventionally, the …
Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays
R Zheng, S Sun, H Liu, H Chen, M Soltanalian… - arXiv preprint arXiv …, 2024 - arxiv.org
Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have
highlighted its superiority over traditional methods, offering faster inference, enhanced super …
highlighted its superiority over traditional methods, offering faster inference, enhanced super …
Coherent, super-resolved radar beamforming using self-supervised learning
High-resolution automotive radar sensors are required to meet the high bar of autonomous
vehicle needs and regulations. However, current radar systems are limited in their angular …
vehicle needs and regulations. However, current radar systems are limited in their angular …
Interpretable and efficient beamforming-based deep learning for single snapshot DOA estimation
R Zheng, S Sun, H Liu, H Chen, J Li - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
We introduce an interpretable deep learning approach for direction of arrival (DOA)
estimation with a single snapshot. Classical subspace-based methods like MUSIC and …
estimation with a single snapshot. Classical subspace-based methods like MUSIC and …