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Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System.

Jin-Cheol Kim¹, Hwi-Gu Jeong¹, Seongwook Lee¹

¹School of Electronics and Information Engineering, College of Engineering, Korea Aerospace University, Goyang-si 10540, Gyeonggi-do, Korea.

Sensors (Basel, Switzerland)

|August 10, 2021

Summary

This summary is machine-generated.

This study introduces a new method for millimeter-wave radar systems to classify targets like pedestrians, cyclists, and cars and determine their movement direction with over 95% accuracy.

Keywords:

millimeter-wave radar moving direction estimation target classification you only look once (YOLO)

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Published on: March 6, 2019

Area of Science:

Engineering
Computer Science
Signal Processing

Background:

Millimeter-wave radar systems are crucial for object detection.
Accurate classification and motion estimation of diverse targets remain challenging.

Purpose of the Study:

To develop a novel method for simultaneous target identification and motion direction determination using millimeter-wave radar.
To adapt radar data for deep learning analysis through image conversion.

Main Methods:

Utilized a 62 GHz frequency-modulated continuous wave (FMCW) radar sensor to collect data from pedestrians, cyclists, and cars.
Developed a data conversion technique to represent radar detections as images for deep learning.
Trained a You Only Look Once (YOLO)-based neural network for classification and direction estimation.

Main Results:

Achieved over 95% accuracy in identifying target types and their movement directions.
Demonstrated an 85% identification accuracy on previously unseen data, indicating good generalization.

Conclusions:

The proposed method effectively integrates target classification and motion direction estimation in FMCW radar systems.
The image conversion technique enhances the applicability of deep learning models for radar data analysis.