A Noise Analysis of 4D RADAR: Robust Sensing for Automotive?

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-04-07 DOI:10.1109/JSEN.2025.3556518

Pak Hung Chan;Sepeedeh Shahbeigi Roudposhti;Xinyi Ye;Valentina Donzella

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引用次数: 0

Abstract

The sensor suite for assisted and automated/ autonomous driving (AAD) functions is critical to the function of a vehicle, but also the first and most significant limitation to the level of automation that the system can achieve. The advancement of 4D RADARs, offering better resolution in both azimuth and elevation compared to traditional RADARs, can assist in achieving more robust situational awareness, while also providing more data for perception algorithms and sensor fusion. However, like all perception sensors, the 4D RADAR is affected by numerous noise factors. To explore the sources of noise, this work identifies, classifies, and analyzes automotive 4D RADAR noise factors. For the first time, 23 noise factors have been considered, in combination with their effect on six 4D RADAR outputs. Finally, this work also proposes and applies a novel dissimilarity metric to collect 4D RADAR data in the presence of rain and snow with different intensities. The proposed metric is used to assess the effect of noise on the variability of the measured data; in addition, it can also be applied to compare any 4D RADAR data. The metric, combined with other pointcloud evaluations, shows that as the rain rate or snow rate intensifies, the size of the pointcloud changes, and the factors in the measurements increase. This work presents the importance of evaluating, compounding, and quantifying noise for 4D RADARs and can pave the way for more in-depth quantitative analysis of modeling and testing of 4D RADARs for assisted and automated driving functions.

查看原文本刊更多论文

四维雷达噪声分析：用于汽车的鲁棒传感？

辅助和自动/自动驾驶（AAD）功能的传感器套件对车辆的功能至关重要，但也是系统可以实现的自动化水平的第一个也是最重要的限制。与传统雷达相比，4D雷达在方位角和仰角上都提供了更好的分辨率，有助于实现更强大的态势感知，同时也为感知算法和传感器融合提供了更多数据。然而，与所有感知传感器一样，4D RADAR也会受到众多噪声因素的影响。为了探索噪声源，本工作对汽车4D RADAR噪声因素进行了识别、分类和分析。这是第一次考虑23个噪声因素，并结合它们对6个4D RADAR输出的影响。最后，本文还提出并应用了一种新的不相似度度量来收集不同强度雨雪条件下的四维雷达数据。提出的度量用于评估噪声对测量数据变异性的影响；此外，它还可以用于比较任何四维雷达数据。该度量与其他点云评价相结合，表明随着降雨率或降雪率的增强，点云的大小发生变化，测量中的因素增加。这项工作提出了评估、复合和量化4D雷达噪声的重要性，可以为4D雷达辅助和自动驾驶功能的建模和测试的更深入的定量分析铺平道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice