Rail Identification Using Camera and Millimeter-Wave Radar Data

Shouyong, Fei; Jimin, Zhang; Xu, Lichao; Zhenhai, Zong; Luo, Jian

doi:10.1109/icitbe54178.2021.00041

Cited by 1 publication

(1 citation statement)

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“…The performance of this system depends on GPS accuracy and GSM or Wi-Fi networks along railway tracks. In another way, cameras and range sensors have been developed to detect railway track obstacles [5]- [8]. The image processing technique and machine learning identify obstacles along the railway track.…”

Section: Introductionmentioning

confidence: 99%

Development of FMCW Radar Signal Processing for High-Speed Railway Collision Avoidance

Putri¹,

Kurniawan²,

Hasanah³

et al. 2022

J. Elektron. dan Telekomun.

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Collision is the main issue in safe transportation, including in the railway system. Sensor systems have been developed to detect obstacles to prevent a collision, such as using cameras. One disadvantage of the camera systems is that performance detection decreases in a not clean environment, like the target position behind the fogs. This paper discusses the development of frequency modulated continuous wave (FMCW) radar signal processing for high-speed railway collision avoidance. The development of radar signal processing combines a two-dimensional constant false alarm rate (2D-CFAR) and robust principal component analysis (RPCA) to detect moving targets under clutter. Cell average (CA) and Greatest of CA (GOCA) CFAR are evaluated under a cluttered wall environment along the railway track. From the experiment, the development of FMCW radar can detect stationary or moving obstacles around 675 meters in front of the locomotive. Combining 2D-CFAR and RPCA algorithm outperforms average background subtraction in extracting moving targets from strong clutter signals along the railway track.

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Section: Introductionmentioning

confidence: 99%