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.

scholarly journals YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2897 ◽  
Author(s):  
Woosuk Kim ◽  
Hyunwoong Cho ◽  
Jongseok Kim ◽  
Byungkwan Kim ◽  
Seongwook Lee
Keyword(s):  
Spatial Clustering ◽  
Radar Data ◽  
Support Vector ◽  
Automotive Radar ◽  
Radar Sensor ◽  
Fmcw Radar ◽  
Radar Systems ◽  
Conventional Methods ◽  
Two Stages ◽  
Deep Learning Model

This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals. In conventional methods, the detection and classification in automotive radar systems are conducted in two successive stages; however, in the proposed method, the two stages are combined into one. To verify the effectiveness of the proposed method, we applied it to the actual radar data measured using our automotive radar sensor. According to the results, our proposed method can simultaneously detect targets and classify them with over 90% accuracy. In addition, it shows better performance in terms of detection and classification, compared with conventional methods such as density-based spatial clustering of applications with noise or the support vector machine. Moreover, the proposed method especially exhibits better performance when detecting and classifying a vehicle with a long body.

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

scholarly journals Planar millimeter wave radar frontend for automotive applications

2003 ◽  
Vol 1 ◽  
pp. 125-129 ◽  
Author(s):  
J. Grubert ◽  
J. Heyen ◽  
C. Metz ◽  
L. C. Stange ◽  
A. F. Jacob
Keyword(s):  
Continuous Wave ◽  
Phase Lock Loop ◽  
Automotive Applications ◽  
Cruise Control ◽  
Fully Integrated ◽  
Fmcw Radar ◽  
Wave Radar ◽  
Feed Network ◽  
Zukünftige Entwicklungen ◽  
77 Ghz

Abstract. A fully integrated planar sensor for 77 GHz automotive applications is presented. The frontend consists of a transceiver multichip module and an electronically steerable microstrip patch array. The antenna feed network is based on a modified Rotman-lens and connected to the array in a multilayer approach offering higher integration. Furthermore, the frontend comprises a phase lock loop to allow proper frequency-modulated continuous wave (FMCW) radar operation. The latest experimental results verify the functionality of this advanced frontend design featuring automatic cruise control, precrash sensing and cut-in detection. These promising radar measurements give reason to a detailed theoretical investigation of system performance. Employing commercially available MMIC various circuit topologies are compared based on signal-tonoise considerations. Different scenarios for both sequential and parallel lobing hint to more advanced sensor designs and better performance. These improvements strongly depend on the availability of suitable MMIC and reliable packaging technologies. Within our present approach possible future MMIC developments are already considered and, thus, can be easily adapted by the flexible frontend design. Es wird ein integrierter planarer Sensor für 77 GHz Radaranwendungen vorgestellt. Das Frontend besteht aus einem Sende- und Empfangs-Multi-Chip-Modul und einer elektronisch schwenkbaren Antenne. Das Speisenetzwerk der Antenne basiert auf einer modifizierten Rotman- Linse. Für eine kompakte Bauweise sind Antenne und Speisenetzwerk mehrlagig integriert. Weiterhin umfasst das Frontend eine Phasenregelschleife für eine präzise Steuerung des frequenzmodulierten Dauerstrichradars. Die aktuellen Messergebnisse bestätigen die Funktionalit¨at dieses neuartigen Frontend-Designs, das automatische Geschwindigkeitsregelung, Kollisionswarnung sowie Nahbereichsüberwachung ermöglicht. Die Qualität der Messergebnisse hat weiterführende theoretische Untersuchungen über die potenzielle Systemleistungsfähigkeit motiviert. Unter Berücksichtigung von kommerziell erhältlichenMMICs werden verschiedene Schaltungstopologien auf der Grundlage des Signal-Rausch-Verhältnisses verglichen. Sowohl für sequenzielle als auch für parallele Ansteuerung der Antennenkeulen wird eine deutliche Leistungssteigerung ermittelt. Diese Verbesserungen hängen maßgeblich von der Verfügbarkeit geeigneter MMICs und einer zuverlässigen Aufbau- und Verbindungstechnik ab. Das vorliegende Frontend-Konzept kann auf Grund seiner Flexibilität leicht an derlei zukünftige Entwicklungen angepasst werden.

scholarly journals Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6202
Author(s):  
Eugin Hyun ◽  
Young-Seok Jin ◽  
Jae-Hyun Park ◽  
Jong-Ryul Yang
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Continuous Wave ◽  
Rigid Motion ◽  
Doppler Spectrum ◽  
Classification Error ◽  
Test Bed ◽  
Radar Sensor ◽  
Detection Scheme ◽  
Wave Radar

In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an ‘extended degree of scattering points’ and a ‘different degree of scattering points’ to represent the characteristics of the non-rigid motion of a moving human in a vehicle. We also design one newly defined feature referred to as the ‘presence of vital signs’, which is related to extracting the Doppler frequency of chest movements due to breathing. Additionally, we use a BDT (Binary Decision Tree) for machine learning during the training and test steps with these three extracted features. We used a 2.45 GHz CW radar front-end module with a single receive antenna and a real-time data acquisition module. Moreover, we built a test-bed with a structure similar to that of an actual vehicle interior. With the test-bed, we measured radar signals in various scenarios. We then repeatedly assessed the classification accuracy and classification error rate using the proposed algorithm with the BDT. We found an average classification accuracy rate of 98.6% for a human with or without motion.

A Highly Modular 77-GHz FMCW Radar Sensor Prototype for Multi Target Tracking Applications

2008 ◽  
Author(s):  
K. Pourvoyeur ◽  
R. Feger ◽  
A. Haderer ◽  
C. Wagner ◽  
A. Stelzer ◽  
...  
Keyword(s):  
Target Tracking ◽  
Radar Sensor ◽  
Fmcw Radar ◽  
Multi Target Tracking ◽  
77 Ghz

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

A fixed IF 77-GHz FMCW radar sensor

2008 ◽  
Author(s):  
A. Haderer ◽  
R. Feger ◽  
J. Schrattenecker ◽  
C. Wagner ◽  
A. Stelzer
Keyword(s):  
Radar Sensor ◽  
Fmcw Radar ◽  
77 Ghz

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.

scholarly journals Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4172 ◽  
Author(s):  
Angelo Coluccia ◽  
Gianluca Parisi ◽  
Alessio Fascista
Keyword(s):  
Continuous Wave ◽  
Low Cost ◽  
Weather Conditions ◽  
Surveillance Systems ◽  
Radar Sensor ◽  
Distributed Sensors ◽  
Fmcw Radar ◽  
Technological Advances ◽  
Spatially Distributed ◽  
New Generation

Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification.

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