Fast chirp FMCW Radar in automotive applications

Tong, Ziqiang; Reuter, Ralf; Fujimoto, Masahiko

doi:10.1049/cp.2015.1362

Cited by 11 publications

(1 citation statement)

References 4 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In commercial FMCW radars, a phase-locked loop is used to linearize FM. Although a feedback-controlled VCO can significantly lower the nonlinearity factor, it is difficult to reduce the FM time below 100 µs [23,24]. Therefore, an openloop concept, such as the predistortion technique [15], is used to realize the FM time in the range of 10-100 µs.…”

Section: Plasma Movementmentioning

confidence: 99%

Fluctuation effects on the plasma density profiles measured by using a frequency modulated continuous wave reflectometer

Seo¹

2022

Plasma Phys. Control. Fusion

View full text Add to dashboard Cite

Scanning diagnostics require that the target should not move during scan for the correct measurement. However, even if a frequency modulated continuous wave (FMCW) reflectometer is operated with a frequency modulation (FM) time of 10 μs, the high temperature fusion plasma is fluctuating even for such a short duration. The fluctuation effect on the reflectometer measurements is investigated by simulating the reflectometer response for various plasma conditions. Because the uncertainty of zero density position might give additional distortion to the reconstructed profile, this effect is also investigated. The simulation results show that the density profile reconstruction is not very sensitive to the zero density position but very sensitive to the fluctuations. The FM rate must be 100 times faster than the fluctuation frequency to suppress the radial distortion below about 20 % of the fluctuation radial amplitude. An algorithm is proposed to reduce the distortion more effectively even at much lower FM rate. The distortion is canceled out by averaging two profiles one of which is measured as increasing the frequency and the other as decreasing the frequency. This algorithm is applied to the experimental data analysis to confirm the simulation results. Through this study, it is concluded that the FM time of the KSTAR reflectometer should be reduced to 2 μs$s to be able to measure the H-mode plasma density profiles.

show abstract

Section: Plasma Movementmentioning

confidence: 99%

Fluctuation effects on the plasma density profiles measured by using a frequency modulated continuous wave reflectometer

Seo¹

2022

Plasma Phys. Control. Fusion

View full text Add to dashboard Cite

show abstract

Radar target classification considering unknown classes using deep convolutional neural network ensemble

Lee

Kang

et al. 2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

The target classification of unknown classes using radar sensor data is discussed. The neural network-based classifier shows high classification accuracy for the learned class targets. However, there is a risk of false decision for the untrained class target owing to an overconfidence problem. The output confidence of the classifier is calibrated using the deep convolutional neural network ensemble structure to propose a method to set the proper threshold for output confidence to decide unknown class targets. When using the proposed method, the accuracy of the learned target is maintained similar to that of the existing single neural network-based classifier, whereas the unknown class target is better identified. Further analysis verifies the effectiveness of the proposed method using commercial automotive radar. The proposed method can classify learned targets with an accuracy of 95% and distinguish unknown class targets with an accuracy of at least 85%. Based on the interaction with other sensors, individual sensors need to make reserved decisions about uncertain information. It is expected that the proposed ensemble network will be efficient in designing the classifier to perform target classification including unknown class decision.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles

et al. 2022

View full text Add to dashboard Cite

The automotive industry is transitioning towards intelligent, connected, and autonomous vehicles to avoid traffic congestion, conflicts, and collisions with increased driver safety. Connected and autonomous vehicles (CAV) must be aware of their surroundings and act as per their environment. Communication infrastructure can be vital in transmitting necessary information to peers and receiving critical information for timely decisions. This article provides a comprehensive review of the topic, covering the aspects of enabling wireless technologies and sensor fusion. The article reviews data acquisition using various sensing devices such as RADAR (Radio Detection and Ranging), LiDAR (Light Detection and Ranging), cameras, and multi-modal sensor fusion of the acquired data after signal processing. Thereafter, it reviews the communication and networking infrastructure for intra-and inter-vehicle communication and related technologies. For each of these themes, research challenges and future directions have been identified. INDEX TERMSSensor fusion, inter-vehicle communication, intra-vehicle communication, environment sensing, road safety, internet of vehicles, connected and autonomous vehicles, 5G networks I. INTRODUCTION

show abstract

Fast chirp FMCW Radar in automotive applications

Cited by 11 publications

References 4 publications

Fluctuation effects on the plasma density profiles measured by using a frequency modulated continuous wave reflectometer

Fluctuation effects on the plasma density profiles measured by using a frequency modulated continuous wave reflectometer

Radar target classification considering unknown classes using deep convolutional neural network ensemble

On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles

Contact Info

Product

Resources

About