77GHz Low-Cost Single-Chip Radar Sensor for Automotive Ground Speed Detection

Nagasaku, Toshiyuki; Kogo, Kenji; Shinoda, Hiroshi; Kondoh, Hiroshi; Muto, Y.; Yamamoto, Akira; Yoshikawa, Takefumi

doi:10.1109/csics.2008.25

Cited by 13 publications

(6 citation statements)

References 3 publications

(1 reference statement)

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“…These algorithms can take the advantages of many efficient FFT-processors that are already available on the market, such as [30]. …”

Section: Resultsmentioning

confidence: 99%

“…Finally, in addition to the results obtained, the accuracy and the cost-effectiveness of the overall solutions, one of the objectives is to design algorithms that facilitate a simple implementation of algorithms in actual SoG systems, rather than the development of a bespoke radar system. These algorithms can take the advantages of many efficient FFT-processors that are already available on the market, such as [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

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Comparison of Adaptive Spectral Estimation for Vehicle Speed Measurement with Radar Sensors

Shariff

Hoare

Daniel

et al. 2017

Sensors

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Vehicle speed-over-ground (SoG) radar offers significant advantages over conventional speed measurement systems. Radar sensors enable contactless speed measurement, which is free from wheel slip. One of the key issues in SoG radar is the development of the Doppler shift estimation algorithm. In this paper, we compared two algorithms to estimate a mean Doppler frequency accurately. The first is the center-of-mass algorithm, which based on spectrum center-of-mass estimation with a bandwidth-limiting technique. The second is the cross-correlation algorithm, which is based on a cross-correlation technique by cross-correlating Doppler spectrum with a theoretical Gaussian curve. Analysis shows that both algorithms are computationally efficient and suitable for real-time SoG systems. Our extensive simulated and experimental results show both methods achieved low estimation error between 0.5% and 1.5% for flat road conditions. In terms of reliability, the cross-correlation method shows good performance under low Signal-to-Noise Ratio (SNR) while the center-of-mass method failed in this condition.

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“…These algorithms can take the advantages of many efficient FFT-processors that are already available on the market, such as [30]. …”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Comparison of Adaptive Spectral Estimation for Vehicle Speed Measurement with Radar Sensors

Shariff

Hoare

Daniel

et al. 2017

Sensors

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“…The RF specifications of the millimeterwave Doppler sensor are listed in Table I. A more detailed description of the sensor chip is found in [9]. …”

Section: Proposed Liquid-level Measurement Methodsmentioning

confidence: 99%

Contactless liquid-level measurement through opaque container using millimeter-wave sensor

Nakagawa

Hyodo

Osada

et al. 2011

2011 IEEE SENSORS Proceedings

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A novel contactless method for measuring liquid level through an opaque container is proposed. A millimeter-wave Doppler sensor is used to "see" through a target container and measure the liquid level on the basis of the absorption of millimeter waves in liquid. One of the challenges is to achieve accurate liquid-level measurement (within sub-millimeter error) despite the inherently large beam-spread angle of the millimeter wave due to diffraction. A piezoelectric vibrator enables accurate measurement by reflecting a limited portion of the spread beam and modulating it to distinguish it from the other portion of the beam. The feasibility of the proposed method for clearly detecting an air-liquid interface concealed in an opaque container was confirmed experimentally. Nonlinearity error of the measured liquid level was within ±0.5 mm, which is the first demonstration of accurate liquid-level measurement using a millimeter-wave sensor.

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“…Compared with vision sensors, the detection performance of millimeter wave (mmWave) radar is less affected by extreme weather [15] [16]. In addition, the mmWave radar not only measures the distance, but also uses the Doppler effect of the signal reflected by the moving object to measure the velocity vector [17] [18]. However, mmWave radar cannot provide the outline information of the object, and it is difficult to distinguish relatively stationary targets.…”

Section: Introductionmentioning

confidence: 99%

MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review

Wei¹,

Zhang²,

Chang³

et al. 2021

Preprint

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With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. Firstly, we introduce the tasks, evaluation criteria and datasets of object detection for autonomous driving. Then, the process of mmWave radar and vision fusion is divided into three parts: sensor deployment, sensor calibration and sensor fusion, which are reviewed comprehensively. Especially, we classify the fusion methods into data level, decision level and feature level fusion methods. Besides, we introduce the fusion of lidar and vision in autonomous driving in the aspects of obstacle detection, object classification and road segmentation, which is promising in the future. Finally, we summarize this article.

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77GHz Low-Cost Single-Chip Radar Sensor for Automotive Ground Speed Detection

Cited by 13 publications

References 3 publications

Comparison of Adaptive Spectral Estimation for Vehicle Speed Measurement with Radar Sensors

Comparison of Adaptive Spectral Estimation for Vehicle Speed Measurement with Radar Sensors

Contactless liquid-level measurement through opaque container using millimeter-wave sensor

MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review

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