Signal processing structure for automotive radar

Folster, Florian; Rohling, Hermann

doi:10.1515/freq.2006.60.1-2.20

Cited by 22 publications

(4 citation statements)

References 3 publications

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“…These two maps are then used in the constant false alarm rate (CFAR) algorithm to identify target point clouds, which are subsequently clustered using a clustering algorithm to output identified targets. Finally, a target tracking algorithm is used to track and correct targets, outputting a list of targets along with their corresponding parameters [ 19 , 20 , 21 , 22 ]. Radar detection results encompass parameters like target energy intensity, range, angle, and velocity, typically represented by parameters such as

, and

.…”

Section: Principles Of Autonomous Driving Radarmentioning

confidence: 99%

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

Huang,

Ding,

Deng

2024

Sensors

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Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods.

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, and

.…”

Section: Principles Of Autonomous Driving Radarmentioning

confidence: 99%

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

Huang,

Ding,

Deng

2024

Sensors

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“…The processing unit uses the captured signal to define target range, angle, and velocity. Based on applications, automotive radar sensors are categorized as short-range radar (SRR) (up to 30 m), medium-range radar (MRR), and long-range radar (LRR) (up to 250 m) [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ]. Automotive radar systems typically run between 24 GHz and 76 GHz portions of the electromagnetic spectrum.…”

Section: Sensorsmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

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Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

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“…Radar has been developed for automotive sensor systems since the 1970s, because of their excellent range resolution, range accuracy, and adaptability in all weather conditions [1, 2]; moreover, has become a key component for self‐driving cars and advanced driver assistance systems, along with other sensors including lidar, ultrasound, cameras etc. Many previous reviews on automotive radar have discussed millimetre‐wave circuit implementation, market analysis, and signal processing [3–7].…”

Section: Introductionmentioning

confidence: 99%

Random phase code for automotive MIMO radars using combined frequency shift keying‐linear FMCW waveform

Kim

2018

IET Radar, Sonar & Navigation

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Signal processing structure for automotive radar

Cited by 22 publications

References 3 publications

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Random phase code for automotive MIMO radars using combined frequency shift keying‐linear FMCW waveform

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