Parking information perception based on automotive millimeter wave SAR

Wang, Chenwei; Pei, Jifang; Li, Minghui; Huang, Yulin; Yang, Jianyu

doi:10.1109/radar.2019.8835793

Cited by 13 publications

(16 citation statements)

References 5 publications

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“…Related works are summarized in Table 13. Advanced radar-based Imaging Methods [245][246][247][248][249][250][251] Environmental information can be obtained intuitively under different illumination and weather condition How to achieving better compatibility with UGVs still remains challenges; Resolution and real-time performance still need to be improved…”

Section: Vehicle Detection: Radar-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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Section: Vehicle Detection: Radar-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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“…Review papers on active imaging used for detecting explosives and monitoring civil infrastructures include [41], [180]. Active imaging recently received attention again in the context of map-like image deep learning and applying existing techniques using a cost effective method in commercial applications [156], [176], [177], [181]. Commercial applications include parking space monitoring and analyzing objects in enclosed packaging for food quality control and non-invasive fault detection.…”

Section: A Collection Systemsmentioning

confidence: 99%

“…In the so called free space scanning method, the forward motion of a radar connected to a moving device, such as a drone or planetary lander, is used to construct images. Sensing application pipelines using this type of scanning either use a large antenna array to cover big areas [161] or a limited number of ingrained low-cost radar device antennas [176], [177].…”

Section: Measured Variablesmentioning

confidence: 99%

“…Low transmission power results in low receiver signal detectability and measurement precision [187]. The IF signal is either used directly in pre-processing [177] or its amplitude [176]- [178], magnitude [175], [179], and or phase values [175]- [178] are used to color the image or for further pre-processing. The authors in [152] measure a lap-joint position with an IF signal component.…”

Section: Measured Variablesmentioning

confidence: 99%

“…and lat. R and v Undefined Radar [146] Doppler shift data (time, TX channel) [190] [63] Spectrogram (range, velocity) Radar [181] Radar image (x, z) Active imaging [191] Radar image (x, z) Active imaging [67] R, ϕ, and vr Undefined Radar [192] Radar image (x, z) Active imaging [193] Radar image (x, z) Active imaging [69] Heartbeat data (time) Radar [189] Radar image (x, z) Active imaging [72] Spectrogram (x, z) [194] [176] Radar image (x, y) Active imaging [177] Radar image (x, y) Active imaging Point cloud frame (x, y) R, vr , θ, and power Undefined [81] Spectrogram (range, velocity) [195] [83] Spectrogram (range, azimuth) Radar [84] Profile (range) Radar Range compressed down-conversion IF data [89] Point cloud frame (x, y, z) Radar [91] Spectrogram (range, velocity) Radar Point cloud frame (x, y, z) vr Undefined [97] Point cloud frame (x, y, z) Radar [99] R and θ Undefined Radar [196] Reflection intensity Undefined Radar Profile (range) Spectrogram (time, frequency) [104] Radar trace Undefined Radar [178] Spectrogram (x, y) Active imaging [105] Heartbeat data (time) [198] [112] Spectrogram (time, frequency) [199] [185] Positioning data Undefined Spatial sweeping [115] R and θ Undefined Radar [153] RSS signal (time, RX channel) Spatial sweeping Profile (range) Waterfall chart (time, range) Dynamic (time-varying) signal components Point cloud frame (x, y, z) vr Undefined Point cloud frame (x, y, z) vr and intensity Undefined [125] Spectrogram (range, velocity) Radar [131] Spectrogram (x, y) Radar…”

Section: B Pre-processingmentioning

confidence: 99%

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Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz that correspond to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.

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Combining MIMO DBF With Automotive Synthetic Aperture Radar Imaging and Phase Error Correction

Farhadi,

Feger,

Fink

et al. 2024

IEEE Access

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Automotive radar systems face challenges in generating high-resolution images that are essential for advancing autonomous driving technology. One promising solution to improve the angle resolution of radar images is the synthetic aperture radar (SAR) technique. However, achieving satisfactory SAR images involves overcoming difficulties such as high computational burden and accurate platform location determination. To address these challenges, we propose an innovative approach that integrates SAR imaging with digital beamforming (DBF) and multiple input multiple output (MIMO) techniques. The proposed approach significantly reduces the computational time required for SAR image formation and demonstrates superior phase error suppression compared to conventional methods. Our implemented algorithm reduces the number of radar samples and imaging complexity by up to a factor of 10 without compromising resolution and image quality. Furthermore, our proposed angle variant phase correction method can be used in challenging automotive scenarios to efficiently mitigate the effects of platform position inaccuracies and undesirable motions. Through simulations and practical experiments, we present promising results to highlight the advantages of combining real and synthetic apertures for radar imaging and phase error correction.

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Parking information perception based on automotive millimeter wave SAR

Cited by 13 publications

References 5 publications

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Combining MIMO DBF With Automotive Synthetic Aperture Radar Imaging and Phase Error Correction

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