← Back to all news
arXiv Machine Learning

Temporal Convolutional Autoencoder for Interference Mitigation in FMCW Radar Altimeters

arXiv:2505. 22783v2 Announce Type: replace-cross Abstract: Reliable altitude estimation with frequency-modulated continuous wave (FMCW) radar altimeters is increasingly a challenge due to in-band interference from modern communication systems.

Related stories

arXiv AI
2d ago

Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing

The paper presents HEAR, a dual‑task Transformer that learns to assess heartbeat observability from mmWave radar phase spectra and simultaneously estimates heart rate. Using a controllable FMCW simulator, the authors generate labeled data where observability is defined by the agreement between the dominant heartbeat‑band peak and the known heart rate. Trained only on simulated data, HEAR transfers zero‑shot to real‑world datasets at 60 and 120 GHz, enabling selective heart‑rate estimation that dramatically reduces error and achieves sub‑50 ms latency on edge hardware.

By Yuxuan Hu, Shilin Shan, Jianfei Yang, Feng Xu
llms
More like this →
arXiv Machine Learning
Sep 22

On Learning Spatial Structure from Pre-Beamforming Per-Antenna Range-Doppler Radar Measurements

This study explores whether spatial structure can be learned directly from pre-beamforming per-antenna range-Doppler (RD) radar measurements, bypassing traditional beamforming steps. Using a 6‑TX × 8‑RX automotive radar with a chirp‑sequence FMCW transmit scheme, the authors train a dual‑chirp shared‑weight encoder on raw RD tensors and evaluate spatial recoverability via bird’s‑eye‑view occupancy maps. Experiments across different transmit configurations (A‑only, B‑only, A+B) and receive apertures demonstrate that meaningful spatial structure is indeed recoverable through learned spatial mixing, without hand‑crafted signal‑processing stages.

By George Sebastian, Philipp Berthold, Bianca Forkel, Leon Pohl, Mirko Maehlisch
multimodal
More like this →
arXiv Machine Learning
Aug 21

A comparison between ceiling-mounted FMCW, IR-UWB and Wi-Fi radar for in-bedroom human activity monitoring and sleep interruption detection

arXiv:2608. 20322v1 Announce Type: new Abstract: Despite their growing importance for contact-free radio frequency (RF) based healthcare monitoring, different radio technologies such as frequency-modulated continuous wave (FMCW) radar, impulse radio ultra-wideband (IR-UWB), and Wi-Fi sensing are rarely compared under identical deployment conditions, as existing studies typically differ in hardware, datasets, and evaluation methodologies.

By Anton Lambrecht, Reda El Hail, Xianjun Jiao, Pieter Crombez, Dominique Schreurs, Peter Karsmakers, Adnan Shahid, Eli De Poorter
More like this →
arXiv Machine Learning
Aug 10

RIS-Aided mmWave Localization Under Cross-Link Interference via Beam-Domain ML Fingerprinting

arXiv:2608. 07444v1 Announce Type: cross Abstract: Accurate user equipment (UE) localization is critical for beam management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) based sixth-generation (6G) networks, especially if the direct base-station-UE links are unavailable.

By Md Tarek Hassan, Dmitry Zelenchuk, Muhammad Ali Babar Abbasi
More like this →
arXiv Machine Learning
Sep 23

Physics-guided deep metric learning with continuous time embeddings for open-world radar pulse de-interleaving

The paper introduces a physics‑guided deep metric learning approach for open‑world radar pulse de‑interleaving, leveraging continuous Time‑of‑Arrival sinusoidal positional encodings to model physical inter‑pulse durations. It builds on a transformer‑based framework, optimizing network parameters solely with Supervised Contrastive learning and employing physics‑based priors—PRI consistency and AoA continuity—for validation and checkpoint selection via unsupervised HDBSCAN clustering. This method aims to improve de‑interleaving performance in dense, contested electromagnetic environments where classical techniques falter.

By Vikas Agnihotri, Jasleen Kaur
llmsrag
More like this →
arXiv Machine Learning
Jun 30

RadarTwin: Scene-Specific mmWave Radar Simulation and Learning for Mobile Indoor Perception

arXiv:2606. 28396v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) radar perception is limited by data scarcity: models trained on existing radar datasets fail to generalize to new objects, environments, and sensing trajectories.

By Emily Bejerano, Federico Tondolo, Devang Gupta, Aaron Mano Cherian, Taeyoo Kim, Ayaan Qayyum, Xiaofan Yu, Xiaofan Jiang
llmsroboticsmultimodal
More like this →