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Data Feature Analysis of Non-Scanning Multi Target Millimeter-Wave Radar in Traffic Flow Detection Applications.

Haiqing Liu¹, Na Li², Deyong Guan³

¹College of Transportation, Shandong University of Science and Technology, Qingdao 266000, China. hqliu@sdust.edu.cn.

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|August 24, 2018

Summary

This summary is machine-generated.

This study explores downward-looking millimeter-wave radar for traffic flow detection. It analyzes vehicle data to support intelligent transportation systems.

Keywords:

FMCW millimeter-wave radar data feature analysis grey correlation analysis traffic flow detection

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Area of Science:

Engineering
Transportation Science
Signal Processing

Background:

Millimeter-wave radar is common in traffic applications.
Limited research exists on downward-looking radar from road-top installations for traffic detection.

Purpose of the Study:

To investigate the feasibility and methods for using millimeter-wave radar installed on road tops to detect traffic flow.
To analyze collected vehicle parameters and their relationships for traffic monitoring.

Main Methods:

Conducting practical experiments to collect vehicle parameters (distance, angle, radar cross-section energy).
Analyzing data features focusing on single parameter characteristics and multi-parameter relationships.
Employing grey correlation analysis to determine correlations between different parameter series.

Main Results:

Detailed analysis of vehicle parameter sampling characteristics.
Identification of multi-parameter relationships crucial for traffic detection.
Quantification of correlations between various data series using grey correlation analysis.

Conclusions:

The study provides a foundational analysis for downward-looking millimeter-wave radar in traffic detection.
Findings support the advancement of intelligent transportation applications like vehicle tracking and traffic flow estimation.
This research offers significant support for future intelligent transportation system developments.