Angular measurements in azimuth and elevation with 77 GHz radar sensors

2013 ◽  
Vol 5 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Klaus Baur ◽  
Marcel Mayer ◽  
Steffen Lutz ◽  
Thomas Walter
Keyword(s):  
Elevation Angle ◽  
Azimuth Angle ◽  
Radar Signal ◽  
Automotive Radar ◽  
Radar Sensor ◽  
Radar Sensors ◽  
Dielectric Lens ◽  
Angular Measurements ◽  
Array Structure ◽  
77 Ghz

An antenna concept for direction of arrival estimation in azimuth and elevation is proposed for 77 GHz automotive radar sensors. This concept uses the amplitude information of the radar signal for the azimuth angle and the phase information for the elevation angle. The antenna consists of a combination of a series-fed-array structure with a cylindrical dielectric lens. This concept is implemented into a radar sensor based on SiGe MMICs for validation. A two- and a four-beam configuration are presented and discussed with respect to angular accuracy and ambiguities.

77-GHz Automotive Radar Sensor System With Antenna Integrated Package

2014 ◽  
Vol 4 (2) ◽  
pp. 352-359 ◽  
Author(s):  
Ka Fai Chang ◽  
Rui Li ◽  
Cheng Jin ◽  
Teck Guan Lim ◽  
Soon Wee Ho ◽  
...  
Keyword(s):  
Sensor System ◽  
Automotive Radar ◽  
Radar Sensor ◽  
77 Ghz

Target modeling and deduction of automotive radar resolution requirements for pedestrian classification

2015 ◽  
Vol 7 (3-4) ◽  
pp. 433-441 ◽  
Author(s):  
Eugen Schubert ◽  
Martin Kunert ◽  
Frank Meinl ◽  
Wolfgang Menzel
Keyword(s):  
Radar Signal ◽  
Automotive Radar ◽  
Radar Sensors ◽  
Sensor Model ◽  
Wide Range ◽  
Collision Mitigation ◽  
Adverse Environmental Conditions ◽  
Accident Scenarios

Pedestrian Collision Mitigation Systems (PCMS) are already in the market for some years. Due to continuously evolving EuroNCAP regulations their presence will increase. Visual sensors, already capable of pedestrian classification, provide functional benefits, because the reaction behavior can be optimized when the imminent collision object is recognized as pedestrian or cyclist. Nevertheless their performance will suffer under adverse environmental conditions like darkness, fog, rain or backlight. Even in such unfavorable situations the performance of radar sensors is not significantly deteriorated. Enabling classification capability to automotive radar will further improve road safety and will lower PCMS's overall costs. In this paper, a multi-reflection-point pedestrian target model based on motion analysis is presented. Together with an appropriate sensor model, pedestrian radar signal responses can be provided for a wide range of accident scenarios. Additionally velocity separation requirements that are needed for classification of pedestrians are derived from the simulations. Besides determination of classification features, the model discloses the limits of classical radar signal processing and further offers the opportunity to evaluate parametric spectral analysis. Based on simulated and measured baseband radar signals of pedestrians one of these techniques is deeper analyzed and its enhancement especially on the velocity separation capability is evaluated.

scholarly journals Pedestrian recognition using automotive radar sensors

2012 ◽  
Vol 10 ◽  
pp. 45-55 ◽  
Author(s):  
A. Bartsch ◽  
F. Fitzek ◽  
R. H. Rasshofer
Keyword(s):  
Machine Learning ◽  
Elevation Angle ◽  
Real Life ◽  
Doppler Frequency ◽  
Radar Data ◽  
Limiting Factors ◽  
Automotive Radar ◽  
Signal Processing Algorithm ◽  
Radar Sensors ◽  
The Impact

Abstract. The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.

scholarly journals Design of a Cyberattack Resilient 77 GHz Automotive Radar Sensor

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 573 ◽  
Author(s):  
Onur Toker ◽  
Suleiman Alsweiss
Keyword(s):  
Autonomous Vehicles ◽  
Electromagnetic Waves ◽  
Continuous Wave ◽  
Velocity Estimation ◽  
Automotive Radar ◽  
Radar Sensor ◽  
Detection Scheme ◽  
Fmcw Radar ◽  
Core Idea ◽  
77 Ghz

In this paper, we propose a novel 77 GHz automotive radar sensor, and demonstrate its cyberattack resilience using real measurements. The proposed system is built upon a standard Frequency Modulated Continuous Wave (FMCW) radar RF-front end, and the novelty is in the DSP algorithm used at the firmware level. All attack scenarios are based on real radar signals generated by Texas Instruments AWR series 77 GHz radars, and all measurements are done using the same radar family. For sensor networks, including interconnected autonomous vehicles sharing radar measurements, cyberattacks at the network/communication layer is a known critical problem, and has been addressed by several different researchers. What is addressed in this paper is cyberattacks at the physical layer, that is, adversarial agents generating 77 GHz electromagnetic waves which may cause a false target detection, false distance/velocity estimation, or not detecting an existing target. The main algorithm proposed in this paper is not a predictive filtering based cyberattack detection scheme where an “unusual” difference between measured and predicted values triggers an alarm. The core idea is based on a kind of physical challenge-response authentication, and its integration into the radar DSP firmware.

System Design of a 77 GHz Automotive Radar Sensor with Superresolution DOA Estimation

2007 ◽  
Author(s):  
Peter Wenig ◽  
Michael Schoor ◽  
Oliver Gunther ◽  
Bin Yang ◽  
Robert Weigel
Keyword(s):  
System Design ◽  
Doa Estimation ◽  
Automotive Radar ◽  
Radar Sensor ◽  
77 Ghz
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