Objects Classification based on UWB Scattered Field and SEM Data using Machine Learning Algorithms

Zaky, Yasmina; Fortino, Nicolas; Dauvignac, Jean-Yves; Miramond, Benoît

doi:10.23919/eurad50154.2022.9784460

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…(3) Although the cost of traditional imaging methods is lower, it is difficult to handle the huge information throughput. The application of artificial intelligence in the field of UWB imaging can improve imaging quality, accelerate processing speed, provide more functionality, and make the system more flexible and adaptable to scenarios with different requirements 41–43 . In the future, we intend to introduce artificial intelligence‐based imaging algorithms, such as deep neural networks in deep learning, to improve the system's generalization, accuracy, and robustness in the future 44 …”

Section: Discussionmentioning

confidence: 99%

Ultra‐wideband imaging with an improved backward projection algorithm for far‐field applications

Xu,

Fan,

et al. 2024

Micro & Optical Tech Letters

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Ultra‐wideband (UWB) radar imaging is widely used in penetration detection, target location and detection, and vital monitoring because of its high interference immunity and excellent target recognition with high resolution. In this paper, we report the design of a UWB stepper radar imaging system by using a vector network analyzer and a specially designed novel Vivaldi antenna for far‐field imaging applications. An anti‐attenuation backward projection (AA‐BP) algorithm is proposed to alleviate the attenuation impact of electromagnetic wave propagation. The AA‐BP algorithm and the specially designed Vivaldi antenna are jointly applied in our experiments on multiple targets through wall imaging in both free space and multiple‐path propagation environments. The experimental results show that our reported imaging system achieves desirable performances of far‐field imaging.

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Section: Discussionmentioning

confidence: 99%

Ultra‐wideband imaging with an improved backward projection algorithm for far‐field applications

Xu,

Fan,

et al. 2024

Micro & Optical Tech Letters

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“…Our first objective is to address the multi-class classification of objects with different geometries using convolutional neural network (CNNs). The choice of CNN was made considering previous studies that highlighted its efficiency compared with other classifiers applied to real data (DT, multiLayer perceptron (MLP), SVM) [22,23]. Classifying an object's shape, regardless of its size, is an interesting task that has not yet been addressed using SEM data.…”

Section: Introductionmentioning

confidence: 99%

Shape and orientation classification of objects based on their electromagnetic signatures using convolutional neural networks

Zaky,

Fortino,

Miramond

et al. 2024

Inverse Problems

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This study addresses the classification of objects using their electromagnetic signatures with Convolutional Neural Networks (CNNs) trained on noiseless data. The singularity expansion method (SEM) was applied to establish a compact model that accurately represents the ultra-wideband scattered field of an object, independently of its orientation and observation angle. To perform the classification, we used a CNN associated with a noise-robust SEM technique to classify different objects based on their characteristic parameters. To validate this approach, we compared the performance of the classifier with and without SEM pre-processing of the scattered field for different noise levels and for object sizes not present in the training set. Moreover, we propose a procedure that determines the direction of the receiving antenna and orientation of an object based on the residues associated with each complex natural resonance. This classification procedure using pre-processed SEM data is promising, especially when generalizing to object sizes not included in the training set.

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“…The classification problem was solved using the first and second CNRs for different airplane scale models. More recently, in [25] the authors tested different ML algorithms to classify PEC targets with simple shapes by using CNRs for synthetic data. In [26] the author extended the application of the workflow to sphere targets of different compositions in addition to PEC.…”

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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Objects Classification based on UWB Scattered Field and SEM Data using Machine Learning Algorithms

Cited by 5 publications

References 14 publications

Ultra‐wideband imaging with an improved backward projection algorithm for far‐field applications

Ultra‐wideband imaging with an improved backward projection algorithm for far‐field applications

Shape and orientation classification of objects based on their electromagnetic signatures using convolutional neural networks

Robust Target Classification Using UWB Sensing^*

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Objects Classification based on UWB Scattered Field and SEM Data using Machine Learning Algorithms

Cited by 5 publications

References 14 publications

Ultra‐wideband imaging with an improved backward projection algorithm for far‐field applications

Ultra‐wideband imaging with an improved backward projection algorithm for far‐field applications

Shape and orientation classification of objects based on their electromagnetic signatures using convolutional neural networks

Robust Target Classification Using UWB Sensing*

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Product

Resources

About

Robust Target Classification Using UWB Sensing^*