Signal Processing for TDM MIMO FMCW Millimeter-Wave Radar Sensors

Li, Xinrong; Wang, Xiaodong; Yang, Qing; Fu, Song

doi:10.1109/access.2021.3137387

Cited by 87 publications

(30 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accordingly, the range resolution in eq. ( 1) is equal to Δ𝑟 =0.6 m. As reported in [24], [29] and detailed in Subsect. IV B, the angular resolution can be roughly evaluated by the formula…”

Section: Radar System Descriptionmentioning

confidence: 86%

“…As regards the spatial resolution of the radar, i.e. the ability to discriminate two closely spaced targets, the range resolution is determined by the classical formula [24], [26]…”

Section: Radar System Descriptionmentioning

confidence: 99%

“…It is based on a focusing in the spatial (range-angle) domain of the raw signals in eq. ( 7) through a beamforming algorithm [24]- [25]. For each scan m, the baseband data frame 𝑠 𝑛 (𝑡), 𝑛 = 0, … ,15, 𝑛 ≠ 8, underdoes a double Fast Fourier Transform (FFT), the first one along time and the second one along the channels.…”

Section: Data Processing Chainmentioning

confidence: 99%

“…Interesting review/tutorial articles with a focus on Multiple Input Multiple Output (MIMO) architectures and signal processing algorithms have been recently published (e.g. see [20]- [24]).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the paper addresses overlooked topics in FMCW radar literature (e.g. see [24]), and thus also aims at providing a comprehensive reference that handles all the pertinent technical information about FMCW radar, starting from basic operation principles up to higher-level signal processing topics such as target detection, clustering, and tracking.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

24 GHz FMCW MIMO Radar for Marine Target Localization: A Feasibility Study

et al. 2022

View full text Add to dashboard Cite

Radar detection and tracking of targets in the marine environment are common tasks performed to ensure the safe navigation of ships or monitor traffic in harbor areas. More recently, radar technology has been proposed to support the collision avoidance system of autonomous surface vehicles, which are characterized by severe constraints in terms of payload and space. The paper investigates the performance of a small and lightweight 24 GHz Frequency Modulated Continuous Wave (FMCW) Multiple-Input Multiple-Output (MIMO) radar, originally developed for automotive applications, to localize marine targets at short range. A complete signal processing strategy is presented combining MIMO radar imaging, detection, and tracking algorithms. The validation of the proposed signal processing chain is firstly performed thanks to numerical tests based on synthetic data. After, results of experimental trials carried out in the marine environment are reported. These results demonstrate that the considered radar together with the adopted signal processing strategy allows the localization of static targets and the tracking of moving targets with satisfactory performance, thus encouraging its use in marine environments.

show abstract

Section: Radar System Descriptionmentioning

confidence: 86%

“…As regards the spatial resolution of the radar, i.e. the ability to discriminate two closely spaced targets, the range resolution is determined by the classical formula [24], [26]…”

Section: Radar System Descriptionmentioning

confidence: 99%

Section: Data Processing Chainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

24 GHz FMCW MIMO Radar for Marine Target Localization: A Feasibility Study

et al. 2022

View full text Add to dashboard Cite

show abstract

Recent Advances in Artificial Intelligence Sensors

Zhang

Wang

Lee

2023

Advanced Sensor Research

View full text Add to dashboard Cite

Significant growth in the development and deployment of artificial intelligence (AI) is being witnessed. Driven by the great versatility of emerging computer science and material science, various AI sensors provide cost‐effective approaches for a wide range of monitoring applications toward the realization of smart homes and personal healthcare. Advanced AI sensors have multiple sensors capable of detecting multidimensional information and human‐brain‐like computation device for data processing. Herein, this review outlines the recent advances in the development of AI sensors. This review first introduces the materials, fabrication methods, and algorithms of current AI sensors and their applications, i.e., complementary metal oxide semiconductor image sensors for computer vision, microelectromechanical systems, microphone sensors for voice recognition, and wearable sensors for gesture recognition. Then, the recent advances in AI wearables sensors and self‐powered sensor systems are highlighted. Next, the current developments of neuromorphic computing systems, multimodality, and digital twins are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the trend of advanced AI sensors is the complementary between edge computing and cloud computing, which will show great potential in the applications of smart buildings, individual healthcare, the Internet of things, etc.

show abstract