New modeling method of millimeter-wave radar considering target radar echo intensity

2021 ◽  
pp. 095440702110045
Author(s):  
Zhan Jun ◽  
Yang Kai ◽  
Dong Xuecai ◽  
Wang Zhangu ◽  
Zhu Huainan ◽  
...  
Keyword(s):  
Real Time ◽  
Millimeter Wave ◽  
Test Evaluation ◽  
The Real ◽  
Millimeter Wave Radar ◽  
Automatic Driving ◽  
Virtual Test ◽  
Wave Radar ◽  
Time Requirements ◽  
Target Visibility

Virtual test evaluation is an important development direction for automatic driving technology testing and evaluation. The millimeter-wave radar sensor model used in virtual test evaluation should meet the real-time and accuracy requirements of the system. The current millimeter-wave radar model can satisfy real-time requirements but cannot simulate the physical characteristics of millimeter-wave radar. This study proposes a millimeter-wave radar model, which introduces the radar cross-sectional (RCS) area judgment index based on geometric clipping extraction and target visibility judgment. Through simulation verification of target visibility, the millimeter-wave radar model integrated with RCS indicators has demonstrated consistent test results with real millimeter-wave radar. The millimeter-wave radar model can better simulate the physical characteristics and error conditions of real millimeter-wave radar as well as enhance the accuracy of virtual simulation tests and evaluation of automatic driving while ensuring the real-time requirements of the simulation.

Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-driven Robotics

2021 ◽  
pp. 1-1
Author(s):  
Christian Schoffmann ◽  
Barnaba Ubezio ◽  
Christoph Boehm ◽  
Stephan Muhlbacher-Karrer ◽  
Hubert Zangl
Keyword(s):  
Real Time ◽  
Millimeter Wave ◽  
Radar Sensor ◽  
Millimeter Wave Radar ◽  
Wave Radar

Near real-time ASL recognition using a millimeter wave radar

2021 ◽  
Author(s):  
Oladipupo Adeoluwa ◽  
Sean J. Kearney ◽  
Emre Kurtoglu ◽  
Charles Connors ◽  
Sevgi Zubeyde Gurbuz
Keyword(s):  
Real Time ◽  
Millimeter Wave ◽  
Millimeter Wave Radar ◽  
Wave Radar

Real-Time Millimeter Wave Radar Simulation

2013 ◽  
Vol 10 (7) ◽  
pp. 337-347 ◽  
Author(s):  
Niklas Peinecke ◽  
Hans-Ullrich Doehler ◽  
Bernd R. Korn
Keyword(s):  
Real Time ◽  
Millimeter Wave ◽  
Millimeter Wave Radar ◽  
Wave Radar

M-Gesture: Person-Independent Real-Time In-Air Gesture Recognition Using Commodity Millimeter Wave Radar

2021 ◽  
pp. 1-1
Author(s):  
Haipeng Liu ◽  
Anfu Zhou ◽  
Zihe Dong ◽  
Yuyang Sun ◽  
Jiahe Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Millimeter Wave ◽  
Gesture Recognition ◽  
Millimeter Wave Radar ◽  
Wave Radar

Radar and vision fusion for the real-time obstacle detection and identification

2019 ◽  
Vol 46 (3) ◽  
pp. 391-395 ◽  
Author(s):  
Xinyu Zhang ◽  
Mo Zhou ◽  
Peng Qiu ◽  
Yi Huang ◽  
Jun Li
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Real Time ◽  
Millimeter Wave ◽  
Obstacle Detection ◽  
Content Type ◽  
Millimeter Wave Radar ◽  
Bounding Box ◽  
Detection And Identification ◽  
Wave Radar

Purpose The purpose of this paper is the presentation and research of a novel sensor fusion-based system for obstacle detection and identification, which uses the millimeter-wave radar to detect the position and velocity of the obstacle. Afterwards, the image processing module uses the bounding box regression algorithm in deep learning to precisely locate and identify the obstacles. Design/methodology/approach Unlike the traditional algorithms that use radar and vision to detect obstacles separately, the purposed method of this paper uses radar to determine the approximate location of obstacles and then uses bounding box regression to achieve accurate positioning and recognition. First, the information of the obstacles can be acquired by the millimeter-wave radar, and the effective target is extracted by filtering the data. Then, use coordinate system conversion and camera parameter calibration to project the effective target to the image plane, and generate the region of interest (ROI). Finally, based on image processing and machine learning techniques, the vehicle targets in the ROI are detected and tracked. Findings The millimeter wave is used to determine the presence of an obstacle, and the deep learning algorithm of the image is combined to determine the shape and the class of the obstacle. The experimental results indicate that the detection rate of this method is up to 91.6 per cent, which can better implement the perception of the environment in front of the vehicle. Originality/value The originality is based on the combination of millimeter-wave sensors and deep learning. Using the bounding box regression algorithm in RCNN, the ROI detected by radar is analyzed to realize real-time obstacle detection and recognition. This method does not require processing the entire image, greatly reducing the amount of data processing and improving the efficiency of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document