activity
20212025
most citedSelf-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

20 citations · 67 across the 9 of their papers we have counts for

collaborators

9 papers

eess.SP2025

Redefining Radar Segmentation: Simultaneous Static-Moving Segmentation and Ego-Motion Estimation using Radar Point Clouds

Simin Zhu, Satish Ravindran, Alexander Yarovoy +1

Conventional radar segmentation research has typically focused on learning category labels for different moving objects. Although fundamental differences between radar and optical…

eess.SP2025

Robust Radar Mounting Angle Estimation in Operational Driving Conditions

Simin Zhu, Satish Ravindran, Lihui Chen +2

The robust estimation of the mounting angle for millimeter-wave automotive radars installed on moving vehicles is investigated. We propose a novel signal processing pipeline that c…

eess.SP2025★ 3 cited

Grouped Target Tracking and Seamless People Counting with a 24 GHz MIMO FMCW

Dingyang Wang, Sen Yuan, Alexander Yarovoy +1

The problem of radar-based tracking of groups of people moving together and counting their numbers in indoor environments is considered here. A novel processing pipeline to track g…

eess.SP2024★ 9 cited

A Deep Automotive Radar Detector using the RaDelft Dataset

Ignacio Roldan, Andras Palffy, Julian F. P. Kooij +3

The detection of multiple extended targets in complex environments using high-resolution automotive radar is considered. A data-driven approach is proposed where unlabeled synchron…

eess.SP2024★ 8 cited

Analysis of Processing Pipelines for Indoor Human Tracking using FMCW radar

Dingyang Wang, Francesco Fioranelli, Alexander Yarovoy

In this paper, the problem of formulating effective processing pipelines for indoor human tracking is investigated, with the usage of a Multiple Input Multiple Output (MIMO) Freque…

eess.SP2024★ 15 cited

See Further Than CFAR: a Data-Driven Radar Detector Trained by Lidar

Ignacio Roldan, Andras Palffy, Julian F. P. Kooij +3

In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with mul…