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EuRAD: Analogue Baseband Processing for Single Chip Radar Proximity Sensor
Proceedings of the 17th European Radar Conference Analogue baseband processing for single chip radar proximity sensor M. van Wanum, M. Polushkin, R. van Dijk TNO, The Netherlands {maurice.vanwanum, michael.polushkin, raymond.vandijk}@tno.nl before the conclusions shows correct operation of the circuit using measurement results. Simulated baseband data is used to show correct operation of the proc
EuRAD: Coherent Multidimensional Agility Radar Signal Processing
Proceedings of the 17th European Radar Conference Coherent Multidimensional Agility Radar Signal Processing Wen-Qin Wang School of Information and Communication Engineering, University of Electronic Science and Technology of China, China wqwang@uestc.edu.cn Abstract—Different from conventional phased-array providing only angle-dependent beampattern, frequency diverse array (FDA) produces angle-ra
EuRAD: The State-of-the-Art of Terahertz Technologies and Applications
Proceedings of the 17th European Radar Conference The State-of-the-Art of Terahertz Technologies and Applications Wei-dong Hu*1, Shi Chen*, Yade Li*, Zhong-bo Zhu*, Yang Liu*, Leo P. Ligthart#^ * School of Information and Electronic Engineering, Beijing Institute of Technology, China # Delft University of Technology, The Netherlands ^ Emeritus Professor 1 hoowind@bit.edu.cn Abstract—The teraher
EuRAD: A 9 to 12.1GHz Sub-Sampling ADPLL Based on a Stochastic Flash TDC and a DCO with a ``Folded'' Common-Mode Resonator Exhibiting Less Than 90fs Jitter and a Peak FoMj of -248dB in 16nm FinFet CMOS
Proceedings of the 17th European Radar Conference A 9 to 12.1 GHz Sub-Sampling ADPLL based on a Stochastic Flash TDC and a DCO with a “Folded” Common-Mode Resonator Exhibiting less than 90fs Jitter and a peak FoMj of -248 dB in 16nm FinFet CMOS R. Levinger, E. Shumaker, A. Farber, S. Bershansky, N. Geron, O. Degani, A. Ravi, R. Banin, E. Banin, J. Kadry and G. Horovitz Wireless Connectivity Solut
EuRAD: Probabilistic Deep Models for Radar Target Recognition
Proceedings of the 17th European Radar Conference Probabilistic Deep Models for Radar Target Recognition Bo Chen, Wenchao Chen National Lab of Radar Signal Processing, Xidian University, China bchen@mail.xidian.edu.cn Abstract —To build a flexible and interpretable model for data analysis, in this talk we introduce our recent work, deep probabilistic autoencoder. In order to provide scalable post
EuRAD: Driving a Zero-Road-Fatality Reality
Proceedings of the 17th European Radar Conference Driving a Zero-Road-Fatality Reality Noam Arkind Arbe, Israel noam.a@arberobotics.com Abstract — The next level of road safety is dependent upon a vehicle’s ability to ‘sense’ the world around it. Unfortunately, most of today’s automotive radar solutions are limited by low spatial resolution which means they cannot generate a clear image of the en
EuRAD: Millimeter-Wave Radar for Touchless Interaction: Soli in the Pixel 4
Proceedings of the 17th European Radar Conference Millimeter-Wave Radar for Touchless Interaction: Soli in the Pixel 4 Jian Wang, Jaime Lien Google LLC, USA wang_jian@ieee.org, jaimelien@alum.mit.edu Abstract — Google’s Pixel 4 launched in fall 2019 with the first radar ever integrated into a smartphone, enabling gesture and presence sensing for new modes of user interaction. These sensing capabi
EuRAD: Radar Signature Prediction with Shooting-and-Bouncing Rays and Hybrid Method
Proceedings of the 17th European Radar Conference Radar Signature Prediction with Shooting-and-Bouncing Rays and Hybrid Method David Prestaux ANSYS, France david.prestaux@ansys.com Abstract — Radar signatures can either be simulated with shooting-and-bouncing rays (SBR) or hybrid full Wave methods. This presentation discusses both results for various applications such as a windmill, drones or a t
EuRAD: Statistical Image Segmentation and Region Classification Approaches for Automotive Radar
Proceedings of the 17th European Radar Conference Statistical Image Segmentation and Region Classification Approaches for Automotive Radar L. Daniel1, Y. Xiao2, E. Hoare3, M. Cherniakov4, M. Gashinova5 Department of Electronic, Electrical and Systems Engineering, University of Birmingham, U.K. {1l.y.daniel, 2YHZ139, 3e.g.hoare, 4m.cherniakov, 5m.s.gashinova}@bham.ac.uk improvement, though rapid a
EuRAD: Deep Neural Network Detection for Pulsed Radar-Embedded M-PSK Communications
Proceedings of the 17th European Radar Conference Deep Neural Network Detection for Pulsed Radar-Embedded M-PSK Communications Christopher Y. Liu, Ric A. Romero Naval Postgraduate School, USA Abstract — In this paper, we investigate the demodulation performance of radar-embedded communications, by utilizing deep neural network (DNN) machine learning to extract the communications symbols. We compa