scholarly journals Minimizing interference in automotive radar using digital beamforming

2011 ◽  
Vol 9 ◽  
pp. 45-48 ◽  
Author(s):  
C. Fischer ◽  
M. Goppelt ◽  
H.-L. Blöcher ◽  
J. Dickmann
Keyword(s):  
Interference Mitigation ◽  
The European Union ◽  
Automotive Radar ◽  
Automotive Safety ◽  
Millimetre Wave ◽  
Digital Beamforming ◽  
Radar Sensors ◽  
Wave Radar ◽  
Automotive Radars ◽  
Planned Activities

Abstract. Millimetre wave radar is an essential part of automotive safety functions. A high interference tolerance, especially with other radar sensors, is vital. This paper gives an overview of the motivation, the boundary conditions and related activities in the MOSARIM project funded by the European Union and concerned with interference mitigation in automotive radars. Current and planned activities considering Digital Beamforming (DBF) as a method for interference mitigation are presented.

scholarly journals Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

2020 ◽  
Author(s):  
Nicolae-Catalin Ristea ◽  
Andrei Anghel ◽  
Radu Tudor Ionescu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Interference Mitigation ◽  
Continuous Wave ◽  
Automotive Radar ◽  
Driver Assistance Systems ◽  
Data Set ◽  
Radar Sensors ◽  
Fully Convolutional Neural Networks

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to compare objectively their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

Ka-band RF MEMS phase shifters for energy starved millimetre-wave radar sensors

2010 ◽  
Author(s):  
R Malmqvist ◽  
C Samuelsson ◽  
B Carlegrim ◽  
P Rantakari ◽  
T Vähä-Heikkilä ◽  
...  
Keyword(s):  
Rf Mems ◽  
Phase Shifters ◽  
Millimetre Wave ◽  
Ka Band ◽  
Radar Sensors ◽  
Wave Radar

VANET-Assisted Interference Mitigation for Millimeter-Wave Automotive Radar Sensors

IEEE Network ◽  
2020 ◽  
Vol 34 (2) ◽  
pp. 238-245
Author(s):  
Mengyuan Zhang ◽  
Shibo He ◽  
Chaoqun Yang ◽  
Jiming Chen ◽  
Junshan Zhang
Keyword(s):  
Millimeter Wave ◽  
Interference Mitigation ◽  
Automotive Radar ◽  
Radar Sensors

scholarly journals Performance Evaluation of Vibrational Measurements through mmWave Automotive Radars

2020 ◽  
Vol 13 (1) ◽  
pp. 98
Author(s):  
Gianluca Ciattaglia ◽  
Adelmo De Santis ◽  
Deivis Disha ◽  
Susanna Spinsante ◽  
Paolo Castellini ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Vibrational Analysis ◽  
Frequency Measurement ◽  
Laser Vibrometer ◽  
Radar Sensors ◽  
Large Bandwidth ◽  
Wave Radar ◽  
Measurement Results ◽  
Reference Laser ◽  
Automotive Radars

Thanks to the availability of a significant amount of inexpensive commercial Frequency Modulated Continuous Wave Radar sensors, designed primarily for the automotive domain, it is interesting to understand if they can be used in alternative applications. It is well known that with a radar system it is possible to identify the micro-Doppler feature of a target, to detect the nature of the target itself (what the target is) or how it is vibrating. In fact, thanks to their high transmission frequency, large bandwidth and very short chirp signals, radars designed for automotive applications are able to provide sub-millimeter resolution and a large detection bandwidth, to the point that it is here proposed to exploit them in the vibrational analysis of a target. The aim is to evaluate what information on the vibrations can be extracted, and what are the performance obtainable. In the present work, the use of a commercial Frequency Modulated Continuous Wave radar is described, and the performances achieved in terms of displacement and vibration frequency measurement of the target are compared with the measurement results obtained through a laser vibrometer, considered as the reference instrument. The attained experimental results show that the radar under test and the reference laser vibrometer achieve comparable outcomes, even in a cluttered scenario.

IMIKO-Radar Project: Laboratory Interference Measurements of Automotive Radar Sensors

2020 ◽  
Author(s):  
Alicja Ossowska ◽  
Leen Sit ◽  
Sarath Manchala ◽  
Thomas Vogler ◽  
Kevin Krupinski ◽  
...  
Keyword(s):  
Automotive Radar ◽  
Radar Sensors ◽  
Interference Measurements

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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