Radar-Based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units

Khushaba, Rami N.; Hill, Andrew

doi:10.1109/lra.2022.3143200

Cited by 22 publications

(3 citation statements)

References 27 publications

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“…Radars are also insensitive to weather and lighting conditions [2]. Radar technologies have been successfully applied in various domains such as virtual reality [58], activity recognition [8], [38], tangible interaction [47], detection of leaks in the ground [59], detection of pedestrian movements [60], detection of material [61], [62], detection of objects [3], [22], and detection of gestures through materials [32], [63], fabrics [40], [42], and liquids [64].…”

Section: B Radar-based Gesture Interaction Through Multiple Materialsmentioning

confidence: 99%

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Sluÿters,

Lambot,

Vanderdonckt

et al. 2024

IEEE Access

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Radar sensing can penetrate non-conducting materials, such as glass, wood, and plastic, which makes it appropriate for recognizing gestures in environments with poor visibility, limited accessibility, and privacy sensitivityWhile the performance of radar-based gesture recognition in these environments has been extensively researched, the preferences that users express for these gestures are less known. To analyze such gestures simultaneously according to their user preference and their system recognition performance, we conducted three gesture elicitation studies each with n 1 =30 participants to identify user-defined, radar-based gestures sensed through three distinct materials: the glass of a shop window, the wood of an office door, and polyvinyl chloride in an emergency. On this basis, we created a new dataset of nine selected gesture classes for n 2 =20 participants repeating twice the same gesture captured by radar through three materials, i.e., glass, wood, and polyvinyl chloride. To uniformly compare recognition rates in these conditions with sensing variations, a specifically tailored procedure was defined and conducted with one-shot radar calibration to train and evaluate a gesture recognizer. 'Wood' achieved the best recognition rate (96.44%), followed by 'Polyvinyl chloride' and 'Glass'. We perform a preference-performance analysis of the gestures by combining the agreement rate from the elicitation studies and the recognition rate from the evaluation.

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Section: B Radar-based Gesture Interaction Through Multiple Materialsmentioning

confidence: 99%

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Sluÿters,

Lambot,

Vanderdonckt

et al. 2024

IEEE Access

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“…This is a highly challenging problem because of the characteristics of the wireless medium; in general, specific solutions for each application and environment must be considered. In particular, Radiofrequency (RF) and Ultra Wideband (UWB) techniques, have gained relevance in this area [9], [10], [11], [12]. Specifically, impulsive UWB systems, which transmit…”

Section: Introduction and Main Contributionsmentioning

confidence: 99%

“…Most solutions in the literature use standard feature extraction techniques that are not specifically tailored to wireless sensing and do not incorporate the benefits of an analytical model. For example, the authors in [11] investigated the performance of centimeterand millimeter-wave radar units for material identification, with antennas in direct contact with the DUT. Eleven materials of different compositions were considered, all of which were set in containers of the same size.…”

Section: Introduction and Main Contributionsmentioning

confidence: 99%

Robust Target Classification Using UWB Sensing^*

2023

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Contactless material characterization has received widespread attention in the radar and engineering domains. Specifically, impulsive Ultra Wideband (UWB) systems are a versatile technology for the nondestructive characterization of samples because the scattered field produced by the targets is highly dependent on their composition and shape. After the initial transient response to the transmitted pulse, the scattered signal can be decomposed as a sum of complex exponentials, called complex natural resonances (CNR), which are dependent only on the geometry and composition of the target. Using this result, a classification problem was formulated to discriminate among targets, and a processing strategy was proposed to solve it. In particular, by using spectral decomposition tools, the information obtained from the physical model can be exploited in combination with data-driven learning techniques. Consequently, a classification strategy that is robust to modeling uncertainties and experimental perturbations was designed. To assess the performance of the new scheme, it was tested using both synthetic and experimental data obtained from targets illuminated with a UWB radar. The results showed substantial gains compared to classification using time-domain signals. INDEX TERMSComplex natural resonances, pattern classification, radar signal processing, spectral analysis.

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Automatic Classification Framework for Neonatal Seizure Using Wavelet Scattering Transform and Nearest Component Analysis

Yadav,

Sharma

2024

IRBM

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Radar-Based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units

Cited by 22 publications

References 27 publications

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Robust Target Classification Using UWB Sensing^*

Automatic Classification Framework for Neonatal Seizure Using Wavelet Scattering Transform and Nearest Component Analysis

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Radar-Based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units

Cited by 22 publications

References 27 publications

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Robust Target Classification Using UWB Sensing*

Automatic Classification Framework for Neonatal Seizure Using Wavelet Scattering Transform and Nearest Component Analysis

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Product

Resources

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Robust Target Classification Using UWB Sensing^*