Investigations on Foam Detection Utilizing Ultra-Broadband Millimeter Wave FMCW Radar

Delden, Marcel van; Westerdick, Stephan; Musch, Thomas

doi:10.1109/imws-amp.2019.8880106

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…Year Application Radar Used [75] Surface sensing mmWave sensor (29.72 GHz-37.7 GHz) [76] Measuring the liquid level and interface sensing mmWave Doppler sensor (77 GHz) [68] Crack detection in ceramic tiles V-Band Imaging Radar (60 GHz) [69] Fluid level sensing TI-IWR1443 (77 GHz) [77] Material classification FMCW radr with Infineon's DEMO-BGT60TR24 sensor (60 GHz) [78] Motion detection and intersection monitoring IWR6843 60 GHz radar [79] Foam detection in chemical applications IC with mmWave ssensor (80 GHz) [10] Obtaining the performance on detecting vibrational targets FMCW 80 GHz sensor integrated on SiGe chip [80] Eavesdropping and spying on phone calls TI-AWR1843BOOST (77 GHz) [70] Material identification TI-IWR1443 FMCW (77 GHz-81 GHz)…”

Section: Referencementioning

confidence: 99%

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

Soumya,

Krishna Mohan,

Cenkeramaddi

2023

Sensors

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Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.

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Section: Referencementioning

confidence: 99%

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

Soumya,

Krishna Mohan,

Cenkeramaddi

2023

Sensors

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“…V. Nyagu paid great attention to studying the dependence of wave propagation on the foam layer thickness at frequencies 6 and 26 GHz in a wide range of foam parameters [12]. In papers [13,14] the authors use millimeter radars to investigate foam structures in cryogenic and industrial chemical plants. By means of remote non-destructive sensing, they find methods of analyzing the parameters of foam coatings.…”

Section: Introductionmentioning

confidence: 99%

Study of electromagnetic waves reflection from foam samples and determination of their dielectric permeativity

Drobakhin,

Filins’kyy

2023

J. Phys. Electron.

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Reflectivity and dielectric permeativity of liquid foam structures with an initial foaming ratio of 55 units in the form of flat-layered foam samples with fixed thicknesses of 40 and 80 mm are under measurement. A multi-frequency technique for studying the foam samples is used. The measuring and computing complex operates in the microwave range from 38 to 52 GHz. It is shown that the foam structure reduces the reflection of electromagnetic waves from metal surfaces significantly. The obtained value of the dielectric permeativity of the foam samples is in the range from 1.08 to 1.26. The results are useful for studying the reflection characteristics offoams and help to determine their dielectric permeativity.

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Study of Microwave Absorption in Foam Structures Using Microstrip Cells

Filins’kyy

2021

2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering (UKRCON)

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Investigations on Foam Detection Utilizing Ultra-Broadband Millimeter Wave FMCW Radar

Cited by 7 publications

References 8 publications

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

Study of electromagnetic waves reflection from foam samples and determination of their dielectric permeativity

Study of Microwave Absorption in Foam Structures Using Microstrip Cells

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